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[A701]
|
S. Sun, L. Feng, and P. Benner.
Data-augmented predictive deep neural network: Enhancing the
extrapolation capabilities of non-intrusive surrogate models.
450:Paper No. 118604, 2026.
[ bib |
DOI ]
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[A700]
|
C. Iglesias-Tesouro, L. Feng, L. Balicki, D. Romano, S. Gugercin, G. Antonini,
G. I. V., and P. Benner.
Learning multivariate matrix-valued electromagnetic transfer
functions using p-AAA.
Research in the Mathematical Science, 2026.
submitted.
[ bib ]
|
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[A699]
|
L. Gkimisis, S. Yildiz, T. Richter, and P. Benner.
A CFL-type condition and theoretical insights for discrete-time
sparse full-order model inference.
482:117269, 2026.
[ bib |
DOI ]
|
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[A698]
|
L. Gkimisis, I. Pontes Duff, P. Goyal, and P. Benner.
On the representation of energy-preserving quadratic operators with
application to Operator Inference.
173:109761, 2026.
[ bib |
DOI ]
|
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[A697]
|
M. Bindhak, A. J. R. Pelling, and J. Saak.
Toward an efficient shifted Cholesky QR for applications in model
order reduction using pyMOR.
26(2):e70143, 2026.
[ bib |
DOI ]
|
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[A696]
|
K.-L. Xu, Z. Li, and P. Benner.
Parametric interpolation model order reduction on Grassmann
manifolds by parallelization.
72(1):198--202, 2025.
[ bib |
DOI ]
|
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[A695]
|
S. Reiter, I. V. Gosea, and S. Gugercin.
Generalizations of data-driven balancing: What to sample for
different balancing-based reduced models.
182:112518, 2025.
[ bib |
DOI ]
|
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[A694]
|
L. Peterson, I. V. Gosea, P. Benner, and K. Sundmacher.
Digital twins in process engineering: An overview on computational
and numerical methods.
Computers & Chemical Engineering, 193:108917, 2025.
[ bib |
DOI ]
|
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[A693]
|
L. Peterson, A. Forootani, E. I. Sanchez Medina, I. V. Gosea, K. Sundamcher,
and P. Benner.
Towards Digital Twins for Power-to-X: Comparing surrogate
models for a catalytic CO2 Methanation reactor.
IEEE Trans. Autom. Sci. Eng., 2025.
[ bib |
DOI ]
|
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[A692]
|
B. Patel, A. Sorrentino, I. V. Gosea, A. C. Antoulas, and
T. Vidaković-Koch.
A data-driven, noise-resilient algorithm for extraction of
distribution of relaxation times using the loewner framework.
Journal of Power Sources, 655:237909, 2025.
[ bib |
DOI ]
|
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[A691]
|
D. S. Karachalios, I. V. Gosea, L. Gkimisis, and A. C. Antoulas.
Data-driven quadratic modeling in the Loewner framework from
input-output time-domain measurements.
24(1):457--500, 2025.
[ bib |
DOI ]
|
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[A690]
|
J. Heiland, Y. Kim, and S. W. R. Werner.
Deep polytopic autoencoders for low-dimensional linear
parameter-varying approximations and nonlinear feedback controller design.
51(6):Paper No. 55, 2025.
[ bib |
DOI ]
|
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[A689]
|
J. Heiland and Y. Kim.
Polytopic autoencoders for very low-dimensional parametrizations of
fluid flow models.
25(2):e70008, 2025.
94th GAMM Annual Meeting.
[ bib |
DOI ]
|
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[A688]
|
J. Heiland and Y. Kim.
Polytopic autoencoders with smooth clustering for reduced-order
modeling of flows.
J. Comput. Phys., 521:113526, 2025.
[ bib |
DOI ]
|
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[A687]
|
L. Gkimisis, N. Aretz, M. Tezzele, T. Richter, P. Benner, and K. E. Willcox.
Non-intrusive reduced-order modeling for dynamical systems with
spatially localized features.
444:118115, 2025.
[ bib |
DOI ]
|
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[A686]
|
A. Carlucci, I. V. Gosea, and S. Grivet-Talocia.
On the generation of SPICE-compatible nonlinear behavioral
macromodels.
15(9):1857--1867, 2025.
[ bib |
DOI ]
|
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[A685]
|
A. Carlucci, I. V. Gosea, and S. Grivet-Talocia.
Data-driven modeling of weakly nonlinear circuits via generalized
transfer function approximation.
IEEE Access, 13:2746--2762, 2025.
[ bib |
DOI ]
|
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[A684]
|
T. Bradde, S. Grivet-Talocia, Q. Aumann, and I. V. Gosea.
A modified AAA algorithm for learning stable reduced-order models
from data.
103(14):1--28, 2025.
[ bib |
DOI ]
|
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[A683]
|
L. Feng, S. Chellappa, and P. Benner.
A posteriori error estimation for model order reduction of parametric
systems.
Adv. Model. and Simul. in Eng. Sci., 11(5), Mar. 2024.
[ bib |
DOI ]
|
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[A682]
|
N. Sarna, J. Giesselmann, and P. Benner.
Data-driven snapshot calibration via monotonic feature matching.
Finite Elements in Analysis and Design, 230:104065, 2024.
[ bib |
DOI ]
|
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[A681]
|
J. Przybilla, I. Pontes Duff, and P. Benner.
Model reduction for second-order systems with inhomogeneous initial
conditions.
183, 2024.
[ bib |
DOI ]
|
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[A680]
|
B. Liljegren-Sailer and I. V. Gosea.
Data-driven and low-rank implementations of Balanced Singular
Perturbation Approximation.
46(1):A483--A507, 2024.
[ bib |
DOI ]
|
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[A679]
|
P. Kergus, I. V. Gosea, and M. Petreczky.
Loewner functions for bilinear systems.
IFAC-PapersOnLine, 58(5):102--107, 2024.
7th IFAC Conference on Analysis and Control of Nonlinear Dynamics and
Chaos ACNDC 2024.
[ bib |
DOI ]
|
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[A678]
|
H. Kapadia, L. Feng, and P. Benner.
Active-learning-driven surrogate modeling for efficient simulation of
parametric nonlinear systems.
419:Paper No. 116657, 36, 2024.
[ bib |
DOI ]
|
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[A677]
|
J. Heiland and Y. Kim.
Convolutional autoencoders, clustering, and POD for low-dimensional
parametrization of flow equations.
175:49--61, 2024.
[ bib |
DOI ]
|
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[A676]
|
S. Grundel and N. Sarna.
Hyper-reduction for parametrized transport dominated problems via
adaptive reduced meshes.
Partial Differential Equations and Applications, 5(1):3, 2024.
[ bib |
DOI ]
|
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[A675]
|
P. Goyal, I. Pontes Duff, and P. Benner.
Dominant subspaces of high-fidelity polynomial structured parametric
dynamical systems and model reduction.
50:42, 2024.
[ bib |
DOI ]
|
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[A674]
|
P. Goyal, B. Peherstorfer, and P. Benner.
Rank-minimizing and structured model inference.
46(3):A1879--A1902, 2024.
[ bib |
DOI ]
|
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[A673]
|
P. Goyal and P. Benner.
Generalized quadratic embeddings for nonlinear dynamics using deep
learning.
Physica D: Nonlinear Phenomena, 463:134158, 2024.
[ bib |
DOI ]
|
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[A672]
|
I. V. Gosea, S. Gugercin, and S. W. R. Werner.
Structured barycentric forms for interpolation-based data-driven
reduced modeling of second-order systems.
50(2):1--32, 2024.
[ bib |
DOI ]
|
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[A671]
|
I. V. Gosea, S. Gugercin, and C. Beattie.
A non-intrusive data-based reformulation of a hybrid projection-based
model reduction method.
IFAC-PapersOnLine, 58(17):226--231, 2024.
26th IFAC Symposium on Mathematical Theory of Networks and Systems
MTNS 2024.
[ bib |
DOI ]
|
|
[A670]
|
L. Gkimisis, T. Richter, and P. Benner.
Adjacency-based, non-intrusive model reduction for vortex-induced
vibrations.
Comp. & Fluids, 275(106248), 2024.
[ bib |
DOI ]
|
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[A669]
|
S. Chellappa, L. Feng, and P. Benner.
Accurate error estimation for model reduction of nonlinear dynamical
systems via data-enhanced error closure.
420:Paper No. 116712, 29, 2024.
[ bib |
DOI ]
|
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[A668]
|
P. Benner, S. Gugercin, and S. W. R. Werner.
Structure-preserving interpolation of quadratic-bilinear systems via
regular multivariate transfer functions.
24(3):e202400048, 2024.
[ bib |
DOI ]
|
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[A667]
|
P. Benner, S. Gugercin, and S. W. R. Werner.
Structured interpolation for multivariate transfer functions of
quadratic-bilinear systems.
50(2):Paper No. 18, 2024.
[ bib |
DOI ]
|
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[A666]
|
P. Benner, P. Goyal, and I. Pontes Duff.
Identification of dominant subspaces for linear structured parametric
systems and model reduction.
2024.
[ bib |
DOI ]
|
|
[A665]
|
R. Torchio, F. Lucchini, M. Filippini, D. Romano, L. Feng, P. Benner, and
G. Antonini.
A reduced order modelling approach for full-Maxwell lightning
strike analyses in layered backgrounds.
38(3):1949--1957, 2023.
[ bib |
DOI ]
|
|
[A664]
|
A. Sorrentino, B. Patel, I. V. Gosea, A. C. Antoulas, and
T. Vidaković-Koch.
Determination of the distribution of relaxation times through
Loewner framework: A direct and versatile approach.
Journal of Power Sources, 585:233575, 2023.
[ bib |
DOI ]
|
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[A663]
|
T. Rüther, I. V. Gosea, L. Jahn, A. C. Antoulas, and M. A. Danzer.
Introducing the Loewner method as a data-driven and
regularization-free approach for the distribution of relaxation times
analysis of Lithium-Ion batteries.
Batteries, 9(2), 2023.
[ bib |
DOI ]
|
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[A662]
|
M. Redmann and I. Pontes Duff.
Model order reduction for bilinear systems with non-zero initial
states -- different approaches with error bounds.
96(6):1491--1504, 2023.
[ bib |
DOI ]
|
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[A661]
|
C. Kweyu, L. Feng, M. Stein, and P. Benner.
Reduced basis method for the nonlinear Poisson-Boltzmann equation
regularized by the range-separated canonical tensor format.
24(8):2915--2935, 2023.
[ bib ]
|
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[A660]
|
J. Heiland and S. W. R. Werner.
Low-complexity linear parameter-varying approximations of
incompressible Navier-Stokes equations for truncated state-dependent
Riccati feedback.
7:3012--3017, 2023.
[ bib |
DOI ]
|
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[A659]
|
S. Grundel and M. Herty.
Model-order reduction for hyperbolic relaxation systems.
24(7):2763--2780, 2023.
[ bib ]
|
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[A658]
|
P. Goyal and P. Benner.
Neural ordinary differential equations with irregular and noisy data.
Roy. Soc. Open Sci., 10(7):221475, 2023.
[ bib |
DOI ]
|
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[A657]
|
L. Gkimisis, T. Richter, and P. Benner.
Adjacency-based, non-intrusive reduced-order modeling for
fluid-structure interactions.
23(4):e202300047, 2023.
[ bib |
DOI ]
|
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[A656]
|
Y. Filanova, I. Pontes Duff, P. Goyal, and P. Benner.
An operator inference oriented approach for linear mechanical
systems.
200(110620), 2023.
[ bib |
DOI ]
|
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[A655]
|
L. Feng, L. Lombardi, G. Antonini, and P. Benner.
Multi-fidelity error estimation accelerates greedy model reduction of
complex dynamical systems.
124(23):5312--5333, 2023.
[ bib |
DOI ]
|
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[A654]
|
L. Feng.
Predicting output responses of nonlinear dynamical systems with
parametrized inputs using LSTM.
IEEE J. Multiscale Multiphysics Comput. Tech., 8:97--107, 2023.
[ bib |
DOI ]
|
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[A653]
|
A. Diaz, M. Heinkenschloss, I. V. Gosea, and A. C. Antoulas.
Interpolatory model reduction of quadratic-bilinear dynamical systems
with quadratic-bilinear outputs.
49:95, 2023.
[ bib |
DOI ]
|
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[A652]
|
S. Chellappa, L. Feng, V. de la Rubia, and P. Benner.
Inf-sup-constant-free state error estimator for model order reduction
of parametric systems in electromagnetics.
71(11):4762--4777, 2023.
[ bib |
DOI ]
|
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[A651]
|
S. Chellappa, B. Cansiz, L. Feng, P. Benner, and M. Kaliske.
Fast and reliable reduced-order models for cardiac electrophysiology.
46:e202370014, 2023.
[ bib |
DOI ]
|
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[A650]
|
P. Benner, K. Lund, and J. Saak.
Towards a benchmark framework for model order reduction in the
Mathematical Research Data Initiative (MaRDI).
23(3):e202300147, 2023.
[ bib |
DOI ]
|
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[A649]
|
P. Benner and J. Heiland.
Space and chaos-expansion Galerkin POD low-order discretization
of PDEs for uncertainty quantification.
124(12):2801--2817, 2023.
[ bib |
DOI ]
|
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[A648]
|
P. Benner, P. Goyal, J. Heiland, and I. Pontes.
A quadratic decoder approach to nonintrusive reduced-order modeling
of nonlinear dynamical systems.
23(1):e202200049, 2023.
[ bib |
DOI ]
|
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[A647]
|
Q. Aumann and S. W. R. Werner.
Structured model order reduction for vibro-acoustic problems using
interpolation and balancing methods.
Journal of Sound and Vibration, 543:117363, 2023.
[ bib |
DOI ]
|
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[A646]
|
Q. Aumann and I. V. Gosea.
Practical challenges in data-driven interpolation: dealing with
noise, enforcing stability, and computing realizations.
Int. J. Adapt. Control Signal Process., pages 1--19, 2023.
[ bib |
DOI ]
|
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[A645]
|
Q. Aumann, P. Benner, J. Saak, and J. Vettermann.
Model order reduction via substructuring for a nonlinear,
differential-algebraic machine tool model with moving loads.
23(1):e202200286, 2023.
[ bib |
DOI ]
|
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[A644]
|
Q. Aumann, P. Benner, I. V. Gosea, J. Saak, and J. Vettermann.
A tangential interpolation framework for the AAA algorithm.
n/a(n/a):e202300183, 2023.
[ bib |
DOI ]
|
|
[A643]
|
V. de la Rubia, S. Chellappa, L. Feng, and P. Benner.
Fast a posteriori state error estimation for reliable frequency
sweeping in microwave circuits via the reduced-basis method.
70(11):5172--5184, 2022.
[ bib |
DOI ]
|
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[A642]
|
J. Vettermann, A. Steinert, C. Brecher, P. Benner, and J. Saak.
Compact thermo-mechanical models for the fast simulation of machine
tools with nonlinear component behavior.
70(8):692--704, 2022.
[ bib |
DOI ]
|
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[A641]
|
M. Redmann and I. Pontes Duff.
Full state approximation by Galerkin projection reduced order
models for stochastic and bilinear systems.
420, 2022.
[ bib |
DOI ]
|
|
[A640]
|
M. A. Khattak, M. I. Ahmad, L. Feng, and B. Benner.
Multivariate moment matching for model order reduction of
quadratic-bilinear systems using error bounds.
9(23), 2022.
[ bib |
DOI ]
|
|
[A639]
|
P. Kergus and I. V. Gosea.
Data-driven approximation and reduction from noisy data in matrix
pencils frameworks.
IFAC-PapersOnLine, 55(30):371--376, 2022.
25th International Symposium on Mathematical Theory of Networks and
Systems, MTNS 2022.
[ bib |
DOI ]
|
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[A638]
|
M. Hund, T. Mitchell, P. Mlinarić, and J. Saak.
Optimization-based parametric model order reduction via
H2 L2 first-order necessary conditions.
44(3):A1554--A1578, 2022.
[ bib |
DOI ]
|
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[A637]
|
J. Heiland and Y. Kim.
Convolutional autoencoders and clustering for low-dimensional
parametrization of incompressible flows.
IFAC-PapersOnLine, 55(30):430--435, 2022.
25th IFAC Symposium on Mathematical Theory of Networks and Systems
MTNS 2022.
[ bib |
DOI |
http ]
|
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[A636]
|
J. Heiland, P. Benner, and R. Bahmani.
Convolutional neural networks for very low-dimensional LPV
approximations of incompressible Navier-Stokes equations.
Frontiers Appl. Math. Stat., 8:879140, 2022.
[ bib |
DOI ]
|
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[A635]
|
P. Goyal and P. Benner.
Discovery of nonlinear dynamical systems using a Runge-Kutta
inspired dictionary-based sparse regression approach.
Philos. Trans. Roy. Soc. A, 478(2262):20210883, 2022.
[ bib |
DOI ]
|
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[A634]
|
I. V. Gosea, S. Gugercin, and C. Beattie.
Data-driven balancing of linear dynamical systems.
44(1):A554--A582, 2022.
[ bib |
DOI ]
|
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[A633]
|
I. V. Gosea and S. Gugercin.
Data-driven modeling of linear dynamical systems with quadratic
output in the AAA framework.
J. Sci. Comput., 91(1):1--28, 2022.
[ bib |
DOI ]
|
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[A632]
|
I. V. Gosea.
Exact and inexact lifting transformations of nonlinear dynamical
systems: Transfer functions, equivalence, and complexity reduction.
Applied Sciences, 12(5):2333, 2022.
[ bib |
DOI ]
|
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[A631]
|
L. Feng, L. Lombardi, P. Benner, D. Romano, and G. Antonini.
Stable model order reduction for delayed PEEC models with
guaranteed accuracy.
IEEE Trans. Circuits Syst. I, Reg. Papers, 69(10):4177--4190,
2022.
[ bib |
DOI ]
|
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[A630]
|
L. Feng, P. Benner, D. Romano, and G. Antonini.
Matrix-free transfer function prediction using model reduction and
machine learning.
70(12):5392--5404, 2022.
[ bib |
DOI ]
|
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[A629]
|
K. Cherifi, P. Goyal, and P. Benner.
A greedy data collection scheme for linear dynamical systems.
Data-Centric Engineering, 3:e16, 2022.
[ bib |
DOI ]
|
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[A628]
|
T. Breiten and B. Unger.
Passivity preserving model reduction via spectral factorization.
142:Paper No. 110368, 12, 2022.
[ bib |
DOI ]
|
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[A627]
|
T. Bendokat and R. Zimmermann.
Geometric optimization for structure-preserving model reduction of
Hamiltonian systems.
IFAC-PapersOnLine: 10th Vienna International Conference on
Mathematical Modelling MATHMOD 2022, 55(20):457--462, 2022.
[ bib |
DOI ]
|
|
[A626]
|
P. Benner, J. Heiland, and S. W. R. Werner.
Robust output-feedback stabilization for incompressible flows using
low-dimensional h_ -controllers.
Comput. Optim. Appl., 82(1):225--249, 2022.
[ bib |
DOI ]
|
|
[A625]
|
P. Benner, P. Goyal, and I. Pontes Duff.
Gramians, energy functionals and balanced truncation for linear
dynamical systems with quadratic outputs.
67(2):886--893, 2022.
[ bib |
DOI ]
|
|
[A624]
|
P. Benner, P. Goyal, J. Heiland, and I. Pontes Duff.
Operator inference and physics-informed learning of low-dimensional
models for incompressible flows.
56:28--51, 2022.
[ bib |
DOI ]
|
|
[A623]
|
I. Pontes Duff and Kürschner.
Numerical computation and new output bounds for time-limited balanced
truncation of discrete-time systems.
623:367--397, Aug. 2021.
[ bib |
DOI ]
|
|
[A622]
|
S. Yildiz, P. Goyal, P. Benner, and B. Karasözen.
Learning reduced-order dynamics for parametrized shallow water
equations from data.
International Journal for Numerical Methods in Fluids,
93(8):2803--2821, 2021.
[ bib |
DOI ]
|
|
[A621]
|
J. Vettermann, S. Sauerzapf, A. Naumann, M. Beitelschmidt, R. Herzog,
P. Benner, and J. Saak.
Model order reduction methods for coupled machine tool models.
MM Science Journal, Special Issue ICTIMT2021 --- 2nd
International Conference on Thermal Issues in Machine Tools, April 20, 2021,
Prague, Czech Republic(3):4652--4659, 2021.
[ bib |
DOI ]
|
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[A620]
|
M. Redmann, C. Bayer, and P. Goyal.
Low-dimensional approximations of high-dimensional asset price
models.
SIAM J. Finan. Math., 12(1):1--28, 2021.
[ bib |
DOI ]
|
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[A619]
|
D. S. Karachalios, I. V. Gosea, and A. C. Antoulas.
The Loewner framework for nonlinear identification and reduction of
Hammerstein cascaded dynamical systems.
20(1):e202000337, 2021.
[ bib |
DOI ]
|
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[A618]
|
R. Jendersie and S. W. R. Werner.
A comparison of numerical methods for model reduction of dense
discrete-time systems.
69(8):683--694, 2021.
[ bib |
DOI ]
|
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[A617]
|
C. Himpe, S. Grundel, and P. Benner.
Model order reduction for gas and energy networks.
Journal of Mathematics in Industry, 11:13, 2021.
[ bib |
DOI ]
|
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[A616]
|
I. V. Gosea, D. S. Karachalios, and A. C. Antoulas.
On computing reduced-order bilinear models from time-domain data.
21(1):e202100254, 2021.
accepted September 2021.
[ bib |
DOI ]
|
|
[A615]
|
I. V. Gosea and S. Güttel.
Algorithms for the rational approximation of matrix-valued functions.
SIAMSciComp, 43(5):A3033--A3054, 2021.
[ bib |
DOI ]
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[A614]
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S. Fresca, L. Dedè, and A. Manzoni.
A comprehensive deep learning-based approach to reduced order
modeling of nonlinear time-dependent parametrized PDEs.
87:61, 2021.
[ bib |
DOI ]
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[A613]
|
L. Feng, G. Fu, and Z. Wang.
A FOM/ROM hybrid approach for accelerating numerical simulations.
89(61), 2021.
[ bib |
DOI ]
|
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[A612]
|
L. Feng and P. Benner.
On error estimation for reduced-order modeling of linear
non-parametric and parametric systems.
55(2):561--594, 2021.
[ bib |
DOI ]
|
|
[A611]
|
S. Chellappa, L. Feng, and P. Benner.
A training set subsampling strategy for the reduced basis method.
89(63):1--34, 2021.
Topical collection dedicated to the ICERM Spring 2020 semester
program on model order reduction.
[ bib |
DOI ]
|
|
[A610]
|
P. Benner and S. W. R. Werner.
Frequency- and time-limited balanced truncation for large-scale
second-order systems.
623:68--103, 2021.
Special issue in honor of P. Van Dooren, Edited by F. Dopico,
D. Kressner, N. Mastronardi, V. Mehrmann, and R. Vandebril.
[ bib |
DOI ]
|
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[A609]
|
P. Benner, S. Gugercin, and S. W. R. Werner.
Structure-preserving interpolation of bilinear control systems.
47(3):43, 2021.
[ bib |
DOI ]
|
|
[A608]
|
P. Benner, S. Gugercin, and S. W. R. Werner.
Structure-preserving interpolation for model reduction of parametric
bilinear systems.
132:109799, 2021.
[ bib |
DOI ]
|
|
[A607]
|
P. Benner and P. Goyal.
Interpolation-based model order reduction for polynomial systems.
43(1):A84--A108, 2021.
[ bib |
DOI ]
|
|
[A606]
|
N. Banagaaya, G. Alì, S. Grundel, and P. Benner.
Index-aware model-order reduction for a special class of nonlinear
differential-algebraic equations.
Journal of Dynamics and Differential Equations, pages 1--25,
2021.
[ bib |
DOI ]
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[A605]
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M. M. A. Asif, M. I. Ahmad, P. Benner, L. Feng, and T. Stykel.
Implicit higher-order moment matching technique for model reduction
of quadratic-bilinear systems.
Journal of the Franklin Institute, 358(3):2015--2038, 2021.
[ bib |
DOI ]
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[A604]
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D. Alfke, L. Feng, L. Lombardi, G. Antonini, and P. Benner.
Model order reduction for delay systems by iterative interpolation.
122(3):684--706, 2021.
[ bib |
DOI ]
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[A603]
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L. Balicki.
Low-rank alternating direction implicit iteration in pyMOR.
GAMM Archive for Students, 2(1):1--13, Feb. 2020.
[ bib |
DOI ]
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[A602]
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K. Xu and Y. Jiang.
Structure-preserving interval-limited balanced truncation reduced
models for port-Hamiltonian systems.
IET Control Theory & Applications, 14(3):405--414, 2020.
[ bib |
DOI ]
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[A601]
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Z. Tomljanović and M. Voigt.
Semi-active H damping optimization by adaptive
interpolation.
27(4):e2300, 2020.
[ bib |
DOI ]
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[A600]
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A. Schmidt and B. Wittwar, D. Haasdonk.
Rigorous and effective a-posteriori error bounds for nonlinear
problems---application to RB methods.
46(2):Paper No. 32, 30, 2020.
[ bib |
DOI ]
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[A599]
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T. K. S. Ritschel, F. Weiß, M. Baumann, and S. Grundel.
Nonlinear model reduction of dynamical power grid models using
quadratization and balanced truncation.
68(12):1022--1034, 2020.
[ bib |
DOI ]
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[A598]
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E. Qian, B. Krämer, B. Peherstorfer, and K. Willcox.
Lift & learn: Physics-informed machine learning for large-scale
nonlinear dynamical systems.
Physica D: Nonlinear Phenomena, 406(1):art. 132401, 2020.
[ bib |
DOI ]
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[A597]
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I. Pontes Duff, S. Grundel, and P. Benner.
New Gramians for linear switched systems: Reachability,
observability, and model reduction.
65(6):2526--2535, 2020.
[ bib |
DOI ]
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[A596]
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C. Kweyu, L. Feng, M. Stein, and P. Benner.
Fast solution of the linearized poisson-boltzmann equation with
nonaffine parametrized boundary conditions using the reduced basis method.
Computing and Visualization in Science, 23:15, 2020.
[ bib |
DOI ]
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[A595]
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G. Kirsten and V. Simoncini.
Order reduction methods for solving large-scale differential matrix
Riccati equations.
42(4):A2182--A2205, 2020.
[ bib |
DOI ]
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[A594]
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Y. Huang, Y.-L. Jiang, and K.-L. Xu.
Structure-preserving model reduction of port-Hamiltonian systems
based on projection.
Asian J. Control, 2020.
[ bib |
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[A593]
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S. M. Hirsch, K. D. Harris, J. N. Kutz, and B. W. Brunton.
Centering data improves the dynamic mode decomposition.
19(3):1920--1955, 2020.
[ bib |
DOI ]
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[A592]
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I. V. Gosea, Q. Zhang, and A. C. Antoulas.
Preserving the DAE structure in the Loewner model reduction and
identification framework.
46(3), 2020.
[ bib |
DOI ]
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[A591]
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S. Fresca, A. Manzoni, L. Dedè, and A. Quarteroni.
Deep learning-based reduced order models in cardiac
electrophysiology.
PLOS ONE, 15(10):1--32, 2020.
[ bib |
DOI ]
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[A590]
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S. Chellappa, L. Feng, and P. Benner.
Adaptive basis construction and improved error estimation for
parametric nonlinear dynamical systems.
121(23):5320--5349, 2020.
Special Issue: Credible High-Fidelity and Low Cost
Simulations in Computational Engineering, Guest Eds.: Giacomini, M. and
Veroy, K. and Dí ez, P.
[ bib |
DOI ]
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[A589]
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X. Cao, P. Benner, I. Pontes Duff, and W. Schilders.
Model order reduction for bilinear control systems with inhomogeneous
initial conditions.
2020.
[ bib |
DOI ]
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[A588]
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P. Benner and S. W. R. Werner.
Hankel-norm approximation of large-scale descriptor systems.
46(3):40, 2020.
[ bib |
DOI ]
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[A587]
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P. Benner, P. Goyal, and P. Van Dooren.
Identification of port-Hamiltonian systems from frequency response
data.
143:104741, 2020.
[ bib |
DOI ]
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[A586]
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P. Benner, P. Goyal, B. Kramer, B. Peherstorfer, and K. Willcox.
Operator inference for non-intrusive model reduction of systems with
non-polynomial nonlinear terms.
372:113433, 2020.
[ bib |
DOI ]
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[A585]
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P. Benner, X. Du, G. Yang, and D. Ye.
Balanced truncation of linear time-invariant systems over
finite-frequency ranges.
46:82, 2020.
[ bib |
DOI ]
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[A584]
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P. Benner, T. Breiten, C. Hartmann, and B. Schmidt.
Model reduction of controlled Fokker-Planck and Liouville-von
Neumann equations.
Journal of Computational Dynamics, 7(1):1--33, 2020.
[ bib |
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[A583]
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C. Beattie, S. Gugercin, and Z. Tomljanović.
Sampling-free model reduction of systems with low-rank
parameterization.
46(6):83, 2020.
[ bib |
DOI ]
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[A582]
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Y. Yue, L. Feng, and P. Benner.
Reduced-order modelling of parametric systems via interpolation of
heterogeneous surrogates.
Advanced Modeling and Simulation in Engineering Sciences, 6:10,
2019.
[ bib |
DOI ]
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[A581]
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J. Saak, D. Siebelts, and S. W. R. Werner.
A comparison of second-order model order reduction methods for an
artificial fishtail.
67(8):648--667, 2019.
[ bib |
DOI ]
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[A580]
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I. Pontes Duff, P. Goyal, and P. Benner.
Balanced truncation for a special class of bilinear descriptor
systems.
IEEE Control Systems Letters, 3(3):535--540, 2019.
[ bib |
DOI ]
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[A579]
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B. Parang, M. Mohammadi, and M. M. Arefi.
Residualisation-based model order reduction in power networks with
penetration of photovoltaic resources.
IET Generation, Transmission & Distribution,
13(13):2619--2626, 2019.
[ bib |
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[A578]
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B. Maboudi Afkham and J. Hesthaven.
Structure-preserving model-reduction of dissipative Hamiltonian
systems.
J. Sci. Comput., 81:3--21, 2019.
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[A577]
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B. Maboudi Afkham and J. S. Hesthaven.
Structure preserving model reduction of parametric Hamiltonian
systems.
39(6):A2616--A2644, 2019.
[ bib |
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[A576]
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B. Kramer and K. E. Willcox.
Nonlinear model order reduction via lifting transformations and
proper orthogonal decomposition.
AIAA Journal, 57(6):2297--2307, 2019.
[ bib |
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[A575]
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D. S. Karachalios, I. V. Gosea, and A. C. Antoulas.
A bilinear identification-modeling framework from time domain data.
19(1):e201900246, 2019.
[ bib |
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[A574]
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Y.-L. Jiang, Z.-Z. Qi, and P. Yang.
Model order reduction of linear systems via the cross Gramian and
SVD.
IEEE Transactions on Circuits and Systems II: Express Briefs,
66(3):422--426, 2019.
[ bib |
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[A573]
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K. Haider, A. Ghafoor, M. Imran, and F. M. Malik.
Time-limited Gramian-based model order reduction for second-order
form systems.
Trans. Inst. Meas. Control, 41(8):2310--2318, 2019.
[ bib |
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[A572]
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S. Grundel, C. Himpe, and J. Saak.
On empirical system Gramians.
19(1):e201900006, 2019.
[ bib |
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[A571]
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P. Goyal and M. Redmann.
Time-limited H2-optimal model order reduction.
355:184--197, 2019.
[ bib |
DOI ]
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[A570]
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L. Feng and P. Benner.
A new error estimator for reduced-order modeling of linear parametric
systems.
IEEE Transactions on Microwave Theory and Techniques,
67(12):4848--4859, 2019.
[ bib |
DOI ]
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[A569]
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P. Buchfink, A. Bhatt, and B. Haasdonk.
Symplectic model order reduction with non-orthonormal bases.
Math. Comput. Appl., 24(2):43, 2019.
[ bib |
DOI ]
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[A568]
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P. Benner, R. Herzog, N. Lang, I. Riedel, and J. Saak.
Comparison of model order reduction methods for optimal sensor
placement for thermo-elastic models.
Eng. Optim., 51(3):465--483, 2019.
[ bib |
DOI ]
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[A567]
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P. Benner and C. Himpe.
Cross-Gramian-based dominant subspaces.
45(5):2533--2553, 2019.
[ bib |
DOI ]
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[A566]
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P. Benner, X. Cao, and W. Schilders.
A bilinear H2 model order reduction approach to linear
parameter-varying systems.
45:2241--2271, 2019.
[ bib |
DOI ]
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[A565]
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R. S. Beddig, P. Benner, I. Dorschky, T. Reis, P. Schwerdtner, M. Voigt, and
S. W. R. Werner.
Model reduction for second-order dynamical systems revisited.
19(1):e201900224, 2019.
[ bib |
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[A564]
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L. Balicki, P. Mlinarić, S. Rave, and J. Saak.
System-theoretic model order reduction with pyMOR.
19(1), 2019.
[ bib |
DOI ]
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[A563]
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M. I. Ahmad, P. Benner, and L. Feng.
Interpolatory model reduction for quadratic-bilinear systems using
error estimators.
Engineering Computations, 36(1):25--44, 2019.
[ bib |
DOI ]
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[A562]
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M. I. Ahmad, P. Benner, and L. Feng.
A new two-sided projection technique for model reduction of
quadratic-bilinear descriptor systems.
International Journal of Computer Mathematics,
96(10):1899--1909, 2019.
[ bib |
DOI ]
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[A561]
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Y. G. I. Acle, F. D. Freitas, N. Martins, and J. Rommes.
Parameter preserving model order reduction of large sparse
small-signal electromechanical stability power system models.
IEEE Transactions on Power Systems, 34(4):2814--2824, 2019.
[ bib |
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[A560]
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X. Wang and M. Yu.
The error bound of timing domain in model order reduction by Krylov
subspace methods.
Journal of Circuits, Systems, and Computers, 27(6):1850093,
2018.
[ bib |
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[A559]
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Z. Tomljanović, C. Beattie, and S. Gugercin.
Damping optimization of parameter dependent mechanical systems by
rational interpolation.
44(6):1797--1820, 2018.
[ bib |
DOI ]
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[A558]
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J. Saak and M. Voigt.
Model reduction of constrained mechanical systems in M-M.E.S.S.
IFAC-PapersOnLine 9th Vienna International Conference on
Mathematical Modelling MATHMOD 2018, Vienna, Austria, 21--23 February
2018, 51(2):661--666, 2018.
[ bib |
DOI ]
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[A557]
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A. C. Rodriguez, S. Gugercin, and J. Boggaard.
Interpolatory model reduction of parameterized bilinear dynamical
systems.
44(6):1887--1916, 2018.
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[A556]
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M. Redmann and P. Kürschner.
An output error bound for time-limited balanced truncation.
121:1--6, 2018.
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[A555]
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C. Poussot-Vassal, D. Quero, and P. Viullemin.
Data-driven approximation of a high fidelity gust-oriented flexible
aircraft dynamical model.
IFAC-PapersOnLine 9th Vienna International Conference on
Mathematical Modelling MATHMOD 2018, Vienna, Austria, 21--23 February
2018, 51(2):559--564, 2018.
[ bib |
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[A554]
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I. Pontes Duff, C. Poussot-Vassal, and C. Seren.
H2-optimal model approximation by input/output-delay
structured reduced-order models.
117:60--67, 2018.
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[A553]
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D. Osipov and K. Sun.
Adaptive nonlinear model reduction for fast power system simulation.
IEEE Transactions on Power Systems, 33(6):6746--6754, 2018.
[ bib |
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[A552]
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X. Meng, Q. Wang, N. Zhou, S. Xiao, and Y. Chi.
Multi-time scale model order reduction and stability consistency
certification of inverter-interfaced DG system in AC microgrid.
Energies, 11(1):254, 2018.
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[A551]
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P. Kürschner.
Balanced truncation model order reduction in limited time intervals
for large systems.
Advances in Computational Mathematics, 44(6):1821--1844, 2018.
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[A550]
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S. Klus, F. Nüske, P. Koltai, H. Wu, I. Kevrekidis, C. Schütte, and
F. Noé.
Data-driven model reduction and transfer operator approximation.
Journal of Nonlinear Science, 28:985--1010, 2018.
[ bib |
DOI ]
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[A549]
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H.-J. Jongsma, P. Mlinarić, S. Grundel, P. Benner, and H. L. Trentelman.
Model reduction of linear multi-agent systems by clustering with
H2 and H error bounds.
30:6, 2018.
[ bib |
DOI ]
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[A548]
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M. Hund and J. Saak.
A connection between time domain model order reduction and moment
matching for LTI systems.
24(5):455--484, 2018.
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[A547]
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M. Hund, P. Mlinarić, and J. Saak.
An H_2 L_2-optimal model order
reduction approach for parametric linear time-invariant systems.
18(1):e201800084, 2018.
[ bib |
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[A546]
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C. Himpe, T. Leibner, and S. Rave.
Hierarchical approximate proper orthogonal decomposition.
40(5):A3267--A3292, 2018.
[ bib |
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[A545]
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C. Himpe.
emgr -- the Empirical Gramian Framework.
Algorithms, 11(7):91, 2018.
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[A544]
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K. Haider, A. Ghafoor, M. Imran, and F. M. Malik.
Frequency interval Gramians based structure preserving model
reduction for second-order systems.
Asian J. Control, 20(2):790--801, 2018.
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[A543]
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I. V. Gosea, M. Petreczky, A. C. Antoulas, and C. Fiter.
Balanced truncation for linear switched systems.
44(6):1845--1886, 2018.
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[A542]
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I. V. Gosea, M. Petreczky, and A. C. Antoulas.
Data-driven model order reduction of linear switched systems in the
Loewner framework.
40(2):B572--B610, 2018.
[ bib |
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[A541]
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I. V. Gosea and A. C. Antoulas.
Data-driven model order reduction of quadratic-bilinear systems.
25(6):e2200, 2018.
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[A540]
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H. Egger, T. Kugler, B. Liljegren-Sailer, M. Marheineke, and V. Mehrmann.
On structure-preserving model reduction for damped wave propagation
in transport networks.
40(1):A331--A365, 2018.
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[A539]
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X. Cheng and J. M. A. Scherpen.
Clustering approach to model order reduction of power networks with
distributed controllers.
Advances in Computational Mathematics, 44(6):1917--1939, 2018.
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[A538]
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X. Cheng, Y. Kawano, and J. M. A. Scherpen.
Model reduction of multi-agent systems using dissimilarity-based
clustering.
2018.
[ bib |
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[A537]
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P. Benner and S. W. R. Werner.
Balancing related model reduction with the MORLAB toolbox.
18(1):e201800083, 2018.
[ bib |
DOI ]
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[A536]
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P. Benner and S. W. R. Werner.
Model reduction of descriptor systems with the MORLAB toolbox.
IFAC-PapersOnLine 9th Vienna International Conference on
Mathematical Modelling MATHMOD 2018, Vienna, Austria, 21--23 February
2018, 51(2):547--552, 2018.
[ bib |
DOI ]
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[A535]
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P. Benner and M. Redmann.
Singular perturbation approximation for linear systems with
Lévy noise.
Stochastics and Dynamics, 18(4):1850033, 2018.
[ bib |
DOI ]
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[A534]
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P. Benner, C. Himpe, and T. Mitchell.
On reduced input-output dynamic mode decomposition.
44(6):1751--1768, 2018.
[ bib |
DOI ]
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[A533]
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P. Benner, P. Goyal, and S. Gugercin.
H2-quasi-optimal model order reduction for
quadratic-bilinear control systems.
39(2):983--1032, 2018.
[ bib |
DOI ]
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[A532]
|
M. Baumann, P. Benner, and J. Heiland.
Space-time Galerkin POD with application in optimal control of
semi-linear parabolic partial differential equations.
40(3):A1611--A1641, 2018.
[ bib |
DOI ]
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[A531]
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B. A. Batten, H. Shoori, J. R. Singler, and M. H. Weerasinghe.
Balanced truncation model reduction of a nonlinear cable-mass PDE
system with interior damping.
Discrete & Continuous Dynamical Systems - B, Online
First:1--25, 2018.
[ bib |
DOI ]
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[A530]
|
N. Banagaaya, P. Benner, L. Feng, P. Meuris, and W. Schoenmaker.
An index-aware parametric model order reduction method for
parameterized quadratic differential-algebraic equations.
Applied Mathematics and Computation, 319:409--424, 2018.
[ bib |
DOI ]
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[A529]
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A. C. Antoulas, P. Benner, and L. Feng.
Model reduction by iterative error system approximation.
24(2):103--118, 2018.
[ bib |
DOI ]
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[A528]
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K. Sato.
Riemannian optimal model reduction of linear second-order systems.
IEEE Contr. Syst. Lett., 1(1):2--7, July 2017.
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[A527]
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H. Zhao, X. Lan, and H. Ren.
Nonlinear power system model reduction based on empirical Gramians.
Journal of Electrical Engineering, 68(6):425--434, 2017.
[ bib |
DOI ]
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[A526]
|
Y. Zhang, L. Feng, A. Seidel-Morgenstern, and P. Benner.
Accelerating optimization and uncertainty quantification of nonlinear
SMB chromatography using reduced-order models.
Computers & Chemical Engineering, pages 237--247, 2017.
[ bib |
DOI ]
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[A525]
|
C. Tolks and C. Ament.
Model order reduction of glucose-insulin homeostasis using empirical
Gramians and balanced truncation.
IFAC-PapersOnline (Proceedings of the 20th IFAC World
Congress), 50(1):14735--14740, 2017.
[ bib |
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[A524]
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N. T. Son and T. Stykel.
Solving parameter-dependent Lyapunov equations using the reduced
basis method with application to parametric model order reduction.
38(2):478--504, 2017.
[ bib |
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[A523]
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T. J. Snowden, P. H. van der Graaf, and M. J. Tindall.
A combined model reduction algorithm for controlled biochemical
systems.
BMC Systems Biology, 11(1):1--18, 2017.
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[A522]
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G. Scarciotti and A. Astolfi.
Nonlinear model reduction by moment matching.
Foundations and Trends in
Systems and Control, 4(3--4):224--409, 2017.
[ bib |
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[A521]
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C. W. Rowley and S. T. M. Dawson.
Model reduction for flow analysis and control.
Annual Review of Fluid Mechanics, 49:387--417, 2017.
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[A520]
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E. Purvine, E. Cotilla-Sanchez, M. Halappanavar, Z. Huang, G. Lin, S. Lu, and
S. Wang.
Comparative study of clustering techniques for real-time dynamic
model reduction.
Statistical Analysis and Data Mining, 10(5):263--276, 2017.
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[A519]
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I. Martini, G. Rozza, and B. Haasdonk.
Certified reduced basis approximation for the coupling of viscous and
inviscid parametrized flow models.
pages 1--23, 2017.
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[A518]
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Y. Levron and J. Belikov.
Reduction of power system dynamic models using sparse
representations.
IEEE Transactions on Power Systems, 32(5):3893--3900, 2017.
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[A517]
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Y. Kawano and J. M. A. Scherpen.
Empirical differential balancing for nonlinear systems.
IFAC-PapersOnLine (Proceedings of the 20th IFAC World
Congress), 50(1):6326--6331, 2017.
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[A516]
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L. Iapichino, S. Ulbrich, and S. Volkwein.
Multiobjective pde-constrained optimization using the reduced-basis
method.
2017.
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[A515]
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C. Himpe, T. Leibner, S. Rave, and J. Saak.
Fast low-rank empirical cross Gramians.
17(1):841--842, 2017.
[ bib |
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[A514]
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K. Haider, A. Ghafoor, M. Imran, and F. M. Malik.
Model reduction of large scale descriptor systems using time limited
Gramians.
Asian J. Control, 19(3):1217--1227, 2017.
[ bib |
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[A513]
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L. Feng, M. Mangold, and P. Benner.
Adaptive POD-DEIM basis construction and its application to a
nonlinear population balance system.
AIChE J., 63(9):3832--3844, 2017.
[ bib |
DOI ]
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[A512]
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L. Feng, A. C. Antoulas, and P. Benner.
Some a posteriori error bounds for reduced order modelling of
(non-)parametrized linear systems.
ESAIM: M2AN, 51(6):2127--2158, 2017.
[ bib |
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[A511]
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G. Dimitriu, R. Ştefănescu, and I. M. Navon.
Comparative numerical analysis using reduced-order modeling
strategies for nonlinear large-scale systems.
Journal of Computational and Applied Mathematics, 310:32--42,
2017.
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[A510]
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X. Cheng, Y. Kawano, and J. M. A. Scherpen.
Reduction of second-order network systems with structure
preservation.
62(10):5026--5038, 2017.
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A. Castagnotto, M. Cruz Varona, L. Jeschek, and B. Lohmann.
sss & sssMOR: Analysis and reduction of large-scale dynamic
systems in MATLAB.
65(2):134--150, 2017.
[ bib |
DOI ]
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[A508]
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J. Bremer, P. Goyal, L. Feng, P. Benner, and K. Sundmacher.
POD-DEIM for efficient reduction of a dynamic 2d catalytic reactor
model.
Computers & Chemical Engineering, 106:777--784, 2017.
[ bib |
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[A507]
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J. Bouvrie and B. Hamzi.
Kernel methods for the approximation of nonlinear systems.
SIAM J. Control Optim., 55(4):2460--2492, 2017.
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[A506]
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P. Benner and S. W. R. Werner.
On the transformation formulas of the Hankel-norm approximation.
17(1):823--824, 2017.
[ bib |
DOI ]
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[A505]
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P. Benner and M. Redmann.
An H2-type error bound for balancing- related model
order reduction of linear systems with Lévy noise.
Systems & Control Letters, 105:1--5, 2017.
[ bib |
DOI ]
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[A504]
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C. Beattie, S. Gugercin, and V. Mehrmann.
Model reduction for systems with inhomogeneous initial conditions.
99:99--106, 2017.
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DOI ]
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[A503]
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J. Annoni and P. Seiler.
A method to construct reduced-order parameter-varying models.
International Journal of Robust and Nonlinear Control,
27(4):582--597, 2017.
[ bib |
DOI ]
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[A502]
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A. Alla and J. N. Kutz.
Nonlinear model order reduction via dynamic mode decomposition.
39(5):B778--B796, 2017.
[ bib |
DOI ]
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[A501]
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M. I. Ahmad, P. Benner, P. Goyal, and J. Heiland.
Moment-matching based model reduction for Navier-Stokes type
quadratic-bilinear descriptor systems.
97(10):1252--1267, 2017.
[ bib |
DOI ]
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[A500]
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M. I. Ahmad, P. Benner, and P. Goyal.
Krylov subspace-based model reduction for a class of bilinear
descriptor systems.
315:303--318, 2017.
[ bib |
DOI ]
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[A499]
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M. I. Ahmad, U. Baur, and P. Benner.
Implicit Volterra series interpolation for model reduction of
bilinear systems.
J. Comput. Appl. Math., 316:15--28, 2017.
[ bib |
DOI ]
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[A498]
|
P. Wittmuess, C. Tarin, A. Keck, E. Arnold, and O. Sawodny.
Parametric model order reduction via balanced truncation with
Taylor series representation.
IEEE Trans. Autom. Control, 61(11):3438--3451, Nov. 2016.
[ bib |
DOI ]
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[A497]
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M. Hund and J. Saak.
A connection between time domain model order reduction and moment
matching.
16(1):727--728, Oct. 2016.
[ bib |
DOI ]
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[A496]
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P. Benner, J. Saak, and M. M. Uddin.
Structure preserving model order reduction of large sparse
second-order index-1 systems and application to a mechatronics model.
22(6):509--523, Aug. 2016.
[ bib |
DOI ]
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[A495]
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P. Benner, J. Saak, and M. M. Uddin.
Balancing based model reduction for structured index-2 unstable
descriptor systems with application to flow control.
Numer. Algebra Control Optim., 6(1):1--20, Mar. 2016.
[ bib |
DOI ]
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[A494]
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P. Benner, P. Kürschner, and J. Saak.
Frequency-limited balanced truncation with low-rank approximations.
38(1):A471--A499, Feb. 2016.
[ bib |
DOI ]
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[A493]
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Z. Zhu, G. Geng, and Q. Jiang.
Power system dynamic model reduction based on extended Krylov
subspace method.
IEEE Transactions on Power Systems, 31(6):4483--4494, 2016.
[ bib |
DOI ]
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[A492]
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T. Wolf and H. Panzer.
The ADI iteration for Lyapunov equations implicitly performs H2
pseudo-optimal model order reduction.
89(3):481--493, 2016.
[ bib |
DOI ]
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[A491]
|
G. Signorini, C. Siviero, S. Grivet-Talocia, and I. S. Stievano.
Macromodeling of I/O buffers via compressed tensor representations
and rational approximations.
6(10):1522--1534, 2016.
[ bib |
DOI ]
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[A490]
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M. Redeker and B. Haasdonk.
A POD-EIM reduced two-scale model for precipitation in porous
media.
22(4):323--344, 2016.
[ bib |
DOI ]
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[A489]
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A. Ramirez, A. Mehrizi-Sani, D. Hussein, M. Matar, M. Abdel-Rahman, J. J.
Chavez, A. Davoudi, and S. Kamalasadan.
Application of balanced realizations for model-order reduction of
dynamic power system equivalents.
IEEE Transactions on Power Delivery, 31(5):2304--2312, 2016.
[ bib |
DOI ]
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[A488]
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J. L. Proctor, S. L. Brunton, and J. N. Kutz.
Generalizing Koopman theory to allow for inputs and control.
17(1):909--930, 2016.
[ bib |
DOI ]
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[A487]
|
J. L. Proctor, S. L. Brunton, and J. N. Kutz.
Dynamic mode decomposition with control.
SIAM J. Applied Dynamical Systems, 15(1):142--161, 2016.
[ bib |
DOI ]
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[A486]
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I. Pontes Duff, S. Gugercin, C. Beattie, C. Poussot-Vassal, and C. Seren.
H2-optimality conditions for reduced time-delay
systems of dimensions one.
IFAC-PapersOnLine, 49(10):7--12, 2016.
13th IFAC on Time Delay Systems TDS 2019.
[ bib |
DOI ]
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[A485]
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K. Perev.
The unifying feature of projection in model order reduction.
Information Technologies and Control, 12(3--4):17--27, 2016.
[ bib |
DOI ]
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[A484]
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L. Peng and K. Mohseni.
Symplectic model reduction of Hamiltonian systems.
38(1):A1--A27, 2016.
[ bib |
DOI ]
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[A483]
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B. Peherstorfer and K. Willcox.
Data-driven operator inference for nonintrusive projection-based
model reduction.
Computer Methods in Applied Mechanics and Engineering,
306:196--215, 2016.
[ bib |
DOI ]
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[A482]
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R. Milk, S. Rave, and F. Schindler.
pyMOR -- generic algorithms and interfaces for model order
reduction.
38(5):S194--S216, 2016.
[ bib |
DOI ]
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[A481]
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M. H. Malik, D. Borzacchiello, F. Chinesta, and P. Diez.
Reduced order modeling for transient simulation of power systems
using tracetory piece-wise linear approximation.
Advanced Modeling and Simulation in Engineering Sciences, 3:31,
2016.
[ bib |
DOI ]
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[A480]
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N. Lang, J. Saak, and T. Stykel.
Balanced truncation model reduction for linear time-varying systems.
22(4):267--281, 2016.
[ bib |
DOI ]
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[A479]
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B. Kramer and S. Gugercin.
Tangential interpolation-based eigensystem realization algorithm for
mimo systems.
22(4):282--306, 2016.
[ bib |
DOI ]
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[A478]
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A. Koskela, E. Jarlebring, and M. E. Hochstenbach.
Krylov approximation of linear odes with polynomial
parameterization.
37(2):519--538, 2016.
[ bib |
DOI ]
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[A477]
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L. Iapichino, S. Volkwein, and A. Wesche.
A-posteriori error analysis for lithium-ion concentrations in
batteries utilizing the reduced-basis method.
22(4):362--379, 2016.
[ bib |
DOI ]
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[A476]
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C. Himpe and M. Ohlberger.
A note on the cross Gramian for non-symmetric systems.
Systems Science and Control Engineering, 4(1):199--208, 2016.
[ bib |
DOI ]
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[A475]
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S. Grivet-Talocia and B. Gustavsen.
Black-box macromodeling and its EMC applications.
IEEE Electroman. Comp. M., 5(3):71--78, 2016.
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[A474]
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M. Grepl, B. Lohmann, and J. Saak.
Editorial.
22(4):265--266, 2016.
[ bib |
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[A473]
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N. Gräbner, V. Mehrmann, S. Quraishi, C. Schröder, and U. von
Wagner.
Numerical methods for parametric model reduction in the simulation of
disk brake squeal.
96(12):1388--1405, 2016.
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DOI ]
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[A472]
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M. Geußand B. Lohmann.
STABLE - a stability algorithm for parametric model reduction by
matrix interpolation.
22(4):307--322, 2016.
[ bib |
DOI ]
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[A471]
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F. D. Freitas, R. Pulch, and J. Rommes.
Fast and accurate model reduction for spectral methods in uncertainty
quantification.
Int. J. for Uncertainty Quantification, 6(3):271--286, 2016.
[ bib |
DOI ]
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[A470]
|
L. Feng, Y. Yue, N. Banagaaya, P. Meuris, W. Schoenmaker, and P. Benner.
Parametric modeling and model order reduction for
(electro-)thermal analysis of nanoelectronic structures.
J. Math. Ind., 6(1):1--10, 2016.
[ bib |
DOI ]
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[A469]
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J. Fehr, J. Heiland, C. Himpe, and J. Saak.
Best practices for replicability, reproducibility and reusability of
computer-based experiments exemplified by model reduction software.
AIMS Mathematics, 1(3):261--281, 2016.
[ bib |
DOI ]
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[A468]
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J. Fehr, P. Holzwarth, and P. Eberhard.
Interface and model reduction for efficient explicit simulations - a
case study with nonlinear vehicle crash models.
22(4):380--396, 2016.
[ bib |
DOI ]
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[A467]
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Z. Drmač and S. Gugercin.
A new selection operator for the discrete empirical interpolation
method---improved a priori error bound and extensions.
38(2):A631--A648, 2016.
[ bib |
DOI ]
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[A466]
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S. Chaturantabut, C. Beattie, and S. Gugercin.
Structure-preserving model reduction for nonlinear port-Hamiltonian
systems.
38(5):B837--B865, 2016.
[ bib |
DOI ]
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[A465]
|
B. W. Brunton, L. A. Johnson, J. G. Ojemann, and J. N. Kutz.
Extracting spatial-temporal coherent patterns in large-scale neural
recordings using dynamic mode decomposition.
Journal of Neuroscience Methods, 258:1--15, 2016.
[ bib |
DOI ]
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[A464]
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T. Breiten.
Structure-preserving model reduction for integro-differential
equations.
54(6):2992--3015, 2016.
[ bib |
DOI ]
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[A463]
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B. Besselink, H. Sandberg, and K. H. Johansson.
Clustering-based model reduction of networked passive systems.
61(10):2958--2973, 2016.
[ bib |
DOI ]
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[A462]
|
P. Benner, P. Kürschner, Z. Tomljanović, and N. Truhar.
Semi-active damping optimization of vibrational systems using the
parametric dominant pole algorithm.
96(5):604--619, 2016.
[ bib |
DOI ]
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[A461]
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P. Benner, S. Grundel, and P. Mlinarić.
Stability preserving model reduction for linearly coupled linear
time-invariant systems.
16(1):817--818, 2016.
[ bib |
DOI ]
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[A460]
|
P. Benner and P. Goyal.
Multipoint interpolation of Volterra series and
H2-model reduction for a family of bilinear descriptor systems.
97:1--11, 2016.
[ bib |
DOI ]
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[A459]
|
P. Benner, T. Damm, M. Redmann, and C. Y. R. R.
Positive operators and stable truncation.
498:74--87, 2016.
[ bib |
DOI ]
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[A458]
|
J. Ballani and D. Kressner.
Reduced basis methods: From low-rank matrices to low-rank tensors.
38(4):A2045--A2067, 2016.
[ bib |
DOI ]
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[A457]
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E. Bader, Z. Zhang, and K. Veroy.
An empirical interpolation approach to reduced basis approximations
for variational inequalities.
22(4):345--361, 2016.
[ bib |
DOI ]
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[A456]
|
A. C. Antoulas, I. V. Gosea, and A. C. Ionita.
Model reduction of bilinear systems in the Loewner framework.
38(5):B889--B916, 2016.
[ bib |
DOI ]
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[A455]
|
M. I. Ahmad, P. Benner, and I. Jaimoukha.
Krylov subspace projection methods for model reduction of
quadratic-bilinear systems.
IET Control Theory & Applications, 10(16):2010--2018, 2016.
[ bib |
DOI ]
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[A454]
|
Y. Zhang, L. Feng, S. Li, and P. Benner.
Accelerating pde constrained optimization by the reduced basis
method: application to batch chromatography.
International Journal for Numerical Methods in Engineering,
104(11):983--1007, 2015.
[ bib |
DOI ]
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[A453]
|
Y. Zhang, L. Feng, S. Li, and P. Benner.
An efficient output error estimation for model order reduction of
parametrized evolution equations.
37(6):B910--B936, 2015.
[ bib |
DOI ]
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[A452]
|
Y. Zhang, L. Feng, S. Li, and P. Benner.
Accelerating PDE constrained optimization by the reduced basis
method: application to batch chromatography.
104(11):983--1007, 2015.
[ bib |
DOI ]
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[A451]
|
C. Wang, H. Yu, P. Li, J. Wu, and C. Ding.
Model order reduction for transient simulation of active distribution
networks.
IET Generation, Transmission & Distribution, 9(5), 2015.
[ bib |
DOI ]
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[A450]
|
R. Pulch, E. J. W. ter Maten, and F. Augustin.
Sensitivity analysis and model order reduction for random linear
dynamical systems.
Math. Comput. Simulat., 111:80--95, 2015.
[ bib |
DOI ]
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[A449]
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R. Pulch and E. J. W. ter Maten.
Stochastic galerkin methods and model order reduction for linear
dynamical systems.
Int. J. for Uncertainty Quantification, 5(3):255--273, 2015.
[ bib |
DOI ]
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[A448]
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A. Paul-Dubois-Taine and D. Amsallem.
An adaptive and efficient greedy procedure for the optimal training
of parametric reduced-order models.
102(5):1262--1292, 2015.
[ bib |
DOI ]
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[A447]
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M. R. Opmeer and T. Reis.
A lower bound for the balanced truncation error for MIMO systems.
60(8):2207--2212, 2015.
[ bib |
DOI ]
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[A446]
|
S. B. Olivadese, G. Signorini, S. Grivet-Talocia, and P. Brenner.
Parameterized and DC-compliant small-signal macromodels of RF
circuit blocks.
5(4):508--522, 2015.
[ bib |
DOI ]
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[A445]
|
I. Martini, G. Rozza, and B. Haasdonk.
Reduced basis approximation and a-posteriori error estimation for the
coupled Stokes-Darcy system.
41(5):1131--1157, 2015.
[ bib |
DOI ]
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[A444]
|
A. Manzoni and F. Negri.
Heuristic strategies for the approximation of stability factors in
quadratically nonlinear parametrized PDEs.
41(5):1255--1288, 2015.
[ bib |
DOI ]
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[A443]
|
M. Mangold, L. Feng, D. Khlopov, S. Palis, P. Benner, D. Binev, and
A. Seidel-Morgenstern.
Nonlinear model reduction of a continuous fluidized bed crystallizer.
Journal of Computational and Applied Mathematics, 289:253--266,
2015.
[ bib |
DOI ]
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[A442]
|
G. De Luca, G. Antonini, and P. Benner.
Parallel model order reduction of sparse electromagnetic/circuit
models.
Appl. Comput. Electrom., 30(1):1--21, 2015.
[ bib |
http ]
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[A441]
|
C. Li, Z. Du, Y. Ni, and G. Zhang.
Reduced model-based coordinated design of decentralized power system
controllers.
31(3):2172--2181, 2015.
[ bib |
DOI ]
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[A440]
|
N. Lang, J. Saak, and T. Stykel.
Towards practical implementations of balanced truncation for LTV
systems.
IFAC-PapersOnLine, 48(1):7--8, 2015.
[ bib |
DOI ]
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[A439]
|
S. Kaulmann, B. Flemisch, B. Haasdonk, K.-A. Lie, and M. Ohlberger.
The localized reduced basis multiscale method for two-phase flows in
porous media.
102(5):1018--1040, 2015.
[ bib |
DOI ]
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[A438]
|
B. Karasözen, T. Küçükseyhan, and M. Uzunca.
Structure preserving integration and model order reduction of
skew-gradient reaction-diffusion systems.
Ann. Oper. Res., pages 1--28, 2015.
[ bib |
DOI ]
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[A437]
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B. Karasözen, C. Akkoyunlu, and M. Uzunca.
Model order reduction for nonlinear Schrödinger equation.
258:509--519, 2015.
[ bib |
DOI ]
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[A436]
|
T. Ishizaki, K. Kashima, A. Girard, J. Imura, L. Chen, and K. Aihara.
Clustered model reduction of positive directed networks.
59:238--247, 2015.
[ bib |
DOI ]
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[A435]
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T. Ishizaki and J. Imura.
Clustered model reduction of interconnected second-order systems.
Nonlinear Theory and Its Applications, IEICE, 6(1):26--37,
2015.
[ bib |
DOI ]
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[A434]
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M. Imran and A. Ghafoor.
Model reduction of descriptor systems using frequency limited
Gramians.
J. Franklin Inst., 352(1):33--51, 2015.
[ bib |
DOI ]
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[A433]
|
C. Himpe and M. Ohlberger.
Data-driven combined state and parameter reduction for inverse
problems.
41(5):1343--1364, 2015.
[ bib |
DOI ]
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[A432]
|
C. Himpe and M. Ohlberger.
The empirical cross Gramian for parametrized nonlinear systems.
IFAC-PapersOnLine (Proceedings of the 8th Vienna International
Conference on Mathematical Modelling), 48(1):727--728, 2015.
[ bib |
DOI ]
|
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[A431]
|
M. W. Hess, S. Grundel, and P. Benner.
Estimating the inf-sup constant in reduced basis methods for
time-harmonic Maxwell's equations.
63(11):3549--3557, 2015.
[ bib ]
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[A430]
|
S. Grundel and L. Jansen.
A joint IMEX-MOR approach for water networks.
IFAC-PapersOnLine, 48(1):260--261, 2015.
[ bib |
DOI ]
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[A429]
|
S. Grivet-Talocia, G. Signorini, S. B. Olivadese, C. Siviero, and P. Brenner.
Thermal noise compliant synthesis of linear lumped macromodels.
5(1):75--85, 2015.
[ bib |
DOI ]
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[A428]
|
L. Giraldi, D. Liu, H. G. Matthies, and A. Nouy.
To be or not to be intrusive? the solution of parametric and
stochastic equations -- proper generalized decomposition.
37(1):A347--A368, 2015.
[ bib |
DOI ]
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[A427]
|
G. M. Flagg and S. Gugercin.
Multipoint Volterra series interpolation and H2
optimal model reduction of bilinear systems.
36(2):549--579, 2015.
[ bib |
DOI ]
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[A426]
|
L. Feng, J. G. Korvink, and P. Benner.
A fully adaptive scheme for model order reduction based on
moment-matching.
IEEE Transactions on Components, Packaging and Manufacturing
Technology, 5(12):1872--1884, 2015.
[ bib |
DOI ]
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[A425]
|
Z. Drmač, S. Gugercin, and C. Beattie.
Vector fitting for matrix-valued rational approximation.
37(5):A2346--A2379, 2015.
[ bib |
DOI ]
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[A424]
|
Z. Drmač, S. Gugercin, and C. Beattie.
Quadrature-based vector fitting for discretized H2
approximation.
37(2):A625--A652, 2015.
[ bib |
DOI ]
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[A423]
|
C. Daversin and C. Prud'homme.
Simultaneous empirical interpolation and reduced basis method for
non-linear problems.
353(12):1105--1109, 2015.
[ bib |
DOI ]
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[A422]
|
K. Carlberg, J. Ray, and B. van Bloemen Waanders.
Decreasing the temporal complexity for nonlinear, implicit
reduced-order models by forecasting.
289:79--103, 2015.
[ bib |
DOI ]
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[A421]
|
O. Burkovska, B. Haasdonk, J. Salomon, and B. Wohlmuth.
Reduced basis methods for pricing options with the Black-Scholes
and Heston model.
SIAM J. Financ. Math., 6(1):685--712, 2015.
[ bib |
DOI ]
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[A420]
|
A. Bruns and P. Benner.
Parametric model order reduction of thermal models using the bilinear
interpolatory rational Krylov algorithm.
21(2):103--129, 2015.
[ bib |
DOI ]
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[A419]
|
T. Breiten, C. Beattie, and S. Gugercin.
Near-optimal frequency-weighted interpolatory model reduction.
78:8--18, 2015.
[ bib |
DOI ]
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[A418]
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T. Bonin, H. Faßbender, A. Soppa, and M. Zaeh.
A fully adaptive rational global Arnoldi method for the model-order
reduction of second-order MIMO systems with proportional damping.
Math. Comput. Simulat., 122, 2015.
[ bib |
DOI ]
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[A417]
|
P. Benner and J. Schneider.
Uncertainty quantification for Maxwell's equations using stochastic
collocation and model order reduction.
International Journal for Uncertainty Quantification,
5(3):195--208, 2015.
[ bib |
DOI ]
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[A416]
|
P. Benner and M. Redmann.
Model reduction for stochastic systems.
Stochastics Partial Differential Equations: Analysis and
Computations, 3(3):291--338, 2015.
[ bib |
DOI ]
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[A415]
|
P. Benner, S. Gugercin, and K. Willcox.
A survey of projection-based model reduction methods for parametric
dynamical systems.
57(4):483--531, 2015.
[ bib |
DOI ]
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[A414]
|
P. Benner, S. Grundel, and N. Hornung.
Parametric model order reduction with a small H2-error
using radial basis functions.
41(5):1231--1253, 2015.
[ bib |
DOI ]
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[A413]
|
P. Benner and S. Grundel.
Model order reduction for a family of linear systems with
applications in parametric and uncertain systems.
39:1--6, 2015.
[ bib |
DOI ]
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[A412]
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P. Benner and L. Feng.
Model order reduction for coupled problems.
Applied and Computational Mathematics: An International
journal, 14(1):3--22, 2015.
[ bib |
http ]
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[A411]
|
P. Benner, E. Dufrechou, P. Ezzatti, E. S. Quintana-Ortí, and A. Remón.
Unleashing GPU acceleration for symmetric band linear algebra
kernels and model reduction.
Cluster Computing, 18(4):1351--1362, 2015.
[ bib |
DOI ]
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[A410]
|
P. Benner and T. Breiten.
Two-sided projection methods for nonlinear model order reduction.
37(2):B239--B260, 2015.
[ bib |
DOI ]
|
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[A409]
|
D. Amsallem, M. Zahr, Y. Choi, and C. Farhat.
Design optimization using hyper-reduced-order models.
Struct. Multidisc. Optim., 51(4):919--940, 2015.
[ bib |
DOI ]
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[A408]
|
K. Ahuja, P. Benner, E. de Sturler, and L. Feng.
Recycling BiCGSTAB with an application to parametric model order
reduction.
SIAM Journal on Scientific Computing, 37(5):S429--S446, 2015.
[ bib |
DOI ]
|
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[A407]
|
M. I. Ahmad, P. Benner, and L. Feng.
A new interpolatory model reduction approach for quadratic bilinear
descriptor systems.
15(1):589--590, 2015.
[ bib |
DOI ]
|
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[A406]
|
U. Baur, C. A. Beattie, and P. Benner.
Mapping parameters across system boundaries: parameterized model
reduction with low-rank variability in dynamics.
14(1):19--22, Dec. 2014.
[ bib |
DOI ]
|
|
[A405]
|
N. Monshizadeh, H. L. Trentelman, and M. K. Camlibel.
Projection-based model reduction of multi-agent systems using graph
partitions.
1(2):145--154, June 2014.
[ bib |
DOI ]
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[A404]
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N. Lang, J. Saak, and P. Benner.
Model order reduction for systems with moving loads.
62(7):512--522, June 2014.
[ bib |
DOI ]
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[A403]
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Z.-H. Xiao and Y.-L. Jiang.
Dimension reduction for second-order systems by general orthogonal
polynomials.
20(4):414--432, 2014.
[ bib |
DOI ]
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[A402]
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D. Wirtz, N. Karajan, and B. Haasdonk.
Surrogate modeling of multiscale models using kernel methods.
Int. J. Numer. Methods Eng., 101(1):1--28, 2014.
[ bib |
DOI ]
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[A401]
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S. Wang, S. Lu, N. Zhou, G. Lin, M. Elizondo, and M. A. Pai.
Dynamic-feature extraction, attribution, and reconstruction (DEAR)
method for power system model reduction.
IEEE Transactions on Power Systems, 29(5):2049--2059, 2014.
[ bib |
DOI ]
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[A400]
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J. H. Tu, C. W. Rowley, D. M. Luchtenburg, S. L. Brunton, and J. N. Kutz.
On dynamic mode decomposition: Theory and applications.
Journal of Computational Dynamics, 1(2):391--421, 2014.
[ bib |
DOI ]
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[A399]
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C. Sturk, L. Vanfretti, Y. Chompoobutrgool, and H. Sandberg.
Coherency-independent structured model reduction of power systems.
IEEE Transactions on Power Systems, 29(5):2418--2426, 2014.
[ bib |
DOI ]
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[A398]
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S. B. Olivadese, S. Grivet-Talocia, C. Siviero, and D. Kaller.
Macromodel-based iterative solvers for simulation of high-speed links
with nonlinear terminations.
4(11):1847--1861, 2014.
[ bib |
DOI ]
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[A397]
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Y. Lu, M. Marheineke, and J. Mohring.
Interpolation-based nonlinear parametric MOR for gas pipelines.
14:971--972, 2014.
[ bib |
DOI ]
|
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[A396]
|
S. Li, Y. Yue, L. Feng, P. Benner, and A. Seidel-Morgenstern.
Model reduction for linear simulated moving bed chromatography
systems using Krylov-subspace methods.
AIChE-J., 60(11):3773--3783, 2014.
[ bib |
DOI ]
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[A395]
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S. Li, L. Feng, P. Benner, and A. Seidel-Morgenstern.
Using surrogate models for efficient optimization of simulated moving
bed chromatography.
Computers & Chemical Engineering, 67:121--132, 2014.
[ bib |
DOI ]
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[A394]
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Y. Konkel, O. Farle, A. Sommer, S. Burgard, and R. Dyczij-Edlinger.
A posteriori error bounds for Krylov-based fast frequency sweeps of
Finite-Element systems.
50(2):441--444, 2014.
[ bib |
DOI ]
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[A393]
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I. Kalashnikova, M. Barone, S. Arunajatesan, and B. van Bloemen Waanders.
Construction of energy-stable projection-based reduced order models.
Applied Mathematics and Computation, 249:569--596, 2014.
[ bib |
DOI ]
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[A392]
|
T. Ishizaki, K. Kashima, J. Imura, and K. Aihara.
Model reduction and clusterization of large-scale bidirectional
networks.
59(1):48--63, Jan. 2014.
[ bib |
DOI ]
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[A391]
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A. C. Ionita and A. C. Antoulas.
Data-driven parametrized model reduction in the Loewner framework.
36(3):A984--A1007, 2014.
[ bib |
DOI ]
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[A390]
|
C. Himpe and M. Ohlberger.
Combined state and parameter reduction.
14(1):825--826, 2014.
[ bib |
DOI ]
|
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[A389]
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C. Himpe and M. Ohlberger.
Cross-Gramian based combined state and parameter reduction for
large-scale control systems.
Mathematical Problems in Engineering, 2014:843869, 2014.
[ bib |
DOI ]
|
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[A388]
|
J. S. Hesthaven, B. Stamm, and S. Zhang.
Efficient greedy algorithms for high-dimensional parameter spaces
with applications to empirical interpolation and reduced basis methods.
ESAIM: Math. Model. Numer. Anal., 48(1):259--283, 2014.
[ bib |
DOI ]
|
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[A387]
|
M. W. Hess and P. Benner.
A reduced basis method for microwave semiconductor devices with
geometric variations.
COMPEL - The International Journal for Computation and
Mathematics in Electrical and Electronic Engineering, 33(4):1071--1081,
2014.
[ bib |
DOI ]
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[A386]
|
L. Giraldi, A. Litvinenko, D. Liu, H. G. Matthies, and A. Nouy.
To be or not to be intrusive? the solution of parametric and
stochastic equations---the “plain vanilla” Galerkin case.
36(6):A2720--A2744, 2014.
[ bib |
DOI ]
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[A385]
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V. Druskin, V. Simoncini, and M. Zaslavsky.
Adaptive tangential interpolation in rational Krylov subspaces for
MIMO dynamical systems.
35(2):476--498, 2014.
[ bib |
DOI ]
|
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[A384]
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P. Benner, M.-S. Hossain, and T. Stykel.
Low-rank iterative methods for periodic projected Lyapunov
equations and their application in model reduction of periodic descriptor
systems.
67(3):669--690, 2014.
[ bib |
DOI ]
|
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[A383]
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P. Benner, L. Feng, W. Schoenmaker, and P. Meuris.
nanoCOPS: Parametric modeling and model order reduction of coupled
problems.
ECMI Newsletter, 56:68--69, 2014.
[ bib |
http ]
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[A382]
|
U. Baur, P. Benner, and L. Feng.
Model order reduction for linear and nonlinear systems: A
system-theoretic perspective.
21(4):331--358, 2014.
[ bib |
DOI ]
|
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[A381]
|
G. Alì, N. Banagaaya, W. H. A. Schilders, and C. Tischendorf.
Index-aware model order reduction for differential-algebraic
equations.
20(4):345--373, 2014.
[ bib |
DOI ]
|
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[A380]
|
P. Benner, N. Lang, and J. Saak.
Modeling structural variability in reduced order models of machine
tool assembly groups via parametric MOR.
13:481--482, Dec. 2013.
[ bib |
DOI ]
|
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[A379]
|
H.-S. Zhao, N. Xue, and N. Shi.
Nonlinear dynamic power system model reduction analysis using
balanced empirical Gramian.
Applied Mechanics and Materials, 448--453:2368--2374, 2013.
[ bib |
DOI ]
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[A378]
|
T. Wolf, H. K. F. Panzer, and B. Lohmann.
Model order reduction by approximate balanced truncation: A unifying
framework.
61(8):545--556, 2013.
[ bib |
DOI ]
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[A377]
|
M. O. Williams, P. J. Schmid, and J. N. Kutz.
Hybrid reduced-order integration with proper orthogonal decomposition
and dynamic mode decomposition.
Multiscale Modeling & Simulation, 11(2):522--544, 2013.
[ bib |
DOI ]
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[A376]
|
M. Petreczky, R. Wisniewski, and J. Leth.
Balanced truncation for linear switched systems.
Nonlinear Analysis: Hybrid Systems, 10:4--20, 2013.
[ bib |
DOI ]
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[A375]
|
M. Ohlberger and S. Rave.
Nonlinear reduced basis approximation of parameterized evolution
equations via the method of freezing.
Comptes Rendus Mathematique, 351(23--24):901--906, 2013.
[ bib |
DOI ]
|
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[A374]
|
C. Nowakowski, P. Kürschner, P. Eberhard, and P. Benner.
Model reduction of an elastic crankshaft for elastic multibody
simulations.
93:198--216, 2013.
Available from http://www.mpi-magdeburg.mpg.de/preprints/.
[ bib |
DOI ]
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[A373]
|
N. Monshizadeh, H. L. Trentelman, and M. K. Camlibel.
Stability and synchronization preserving model reduction of
multi-agent systems.
62(1):1--10, 2013.
[ bib |
DOI ]
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[A372]
|
C. E. Lieberman, K. Fidkowski, K. Willcox, and B. Van Bloemen Waanders.
Hessian-based model reduction: large-scale inversion and prediction.
Int. J. Numer. Methods Fluids, 71(2):135--150, 2013.
[ bib |
DOI ]
|
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[A371]
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C. Himpe and M. Ohlberger.
A unified software framework for empirical Gramians.
J. Math., 2013:1--6, 2013.
[ bib |
DOI ]
|
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[A370]
|
M. W. Hess and P. Benner.
Fast evaluation of time-harmonic Maxwell's equations using the
reduced basis method.
61(6):2265--2274, 2013.
[ bib |
DOI ]
|
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[A369]
|
B. Haasdonk.
Convergence rates of the POD-Greedy method.
47(3):859--873, 2013.
[ bib ]
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[A368]
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C. Guiver and M. R. Opmeer.
Error bounds in the gap metric for dissipative balanced
approximations.
439(12):3659--3698, 2013.
[ bib |
DOI ]
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[A367]
|
S. Gugercin, T. Stykel, and S. Wyatt.
Model reduction of descriptor systems by interpolatory projection
methods.
35(5):B1010--B1033, 2013.
[ bib |
DOI ]
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[A366]
|
M. Geuss, H. Panzer, and B. Lohmann.
On parametric model order reduction by matrix interpolation.
Proceedings of the 12th European Control Conference, pages
3433--3438, 2013.
[ bib |
DOI ]
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[A365]
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G. M. Flagg, C. A. Beattie, and S. Gugercin.
Interpolatory H model reduction.
62(7):567--574, 2013.
[ bib ]
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[A364]
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L. Feng, P. Benner, and J. G. Korvink.
Subspace recycling accelerates the parametric macromodeling of
MEMS.
94(1):84--110, 2013.
[ bib |
DOI ]
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[A363]
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B. Besselink, U. Tabak, A. Lutowska, N. van de Wouw, H. Nijmeijer, D. J. Rixen,
M. E. Hochstenbach, and W. H. A. Schilders.
A comparison of model reduction techniques from structural dynamics,
numerical mathematics and systems and control.
Journal of Sound and Vibration, 332(19):4403--4422, 2013.
[ bib |
DOI ]
|
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[A362]
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P. Benner, Z. Tomljanović, and N. Truhar.
Optimal damping of selected eigenfrequencies using dimension
reduction.
20(1):1--17, 2013.
[ bib |
DOI ]
|
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[A361]
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P. Benner, P. Kürschner, and J. Saak.
A reformulated low-rank ADI iteration with explicit residual
factors.
13(1):585--586, 2013.
[ bib |
DOI ]
|
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[A360]
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P. Benner, P. Kürschner, and J. Saak.
An improved numerical method for balanced truncation for symmetric
second order systems.
19(6):593--615, 2013.
[ bib |
DOI ]
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[A359]
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B. Anić, C. Beattie, S. Gugercin, and A. C. Antoulas.
Interpolatory weighted-H2 model reduction.
49(5):1275--1280, 2013.
[ bib |
DOI ]
|
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[A358]
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P. Benner and M. W. Hess.
The reduced basis method for time-harmonic Maxwell's equations.
12(1):661--662, Dec. 2012.
[ bib |
DOI ]
|
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[A357]
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S. Waldherr and B. Haasdonk.
Efficient parametric analysis of the chemical master equation through
model order reduction.
BMC Systems Biology, 6:81, 2012.
[ bib |
DOI ]
|
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[A356]
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M. M. Uddin, J. Saak, B. Kranz, and P. Benner.
Computation of a compact state space model for an adaptive spindle
head configuration with piezo actuators using balanced truncation.
Production Engineering, 6:577--586, 2012.
[ bib |
DOI ]
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[A355]
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C. Teng.
Second-order model reduction based on gramians.
J. Control Sci. Eng., 2012:1--9, 2012.
[ bib |
DOI ]
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[A354]
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G. Serre, P. Lafon, X. Gloerfelt, and C. Bailly.
Reliable reduced-order models for time-dependent linearized Euler
equations.
Journal of Computational Physics, 231(15):5176--5194, 2012.
[ bib |
DOI ]
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[A353]
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A. C. Or, J. L. Speyer, and J. Kim.
Reduced balancing transformations for large nonnormal state-space
systems.
J. Guid. Control Dyn., 35(1):129--137, 2012.
[ bib |
DOI ]
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[A352]
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M. R. Opmeer.
Model order reduction by balanced proper orthogonal decomposition and
by rational interpolation.
57(2):472--477, 2012.
[ bib ]
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[A351]
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Y.-L. Jiang and H.-B. Chen.
Time domain model order reduction of general orthogonal polynomials
for linear input-output systems.
57(2):330--343, 2012.
[ bib ]
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[A350]
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S. Gugercin, R. V. Polyuga, C. Beattie, and A. van der Schaft.
Structure-preserving tangential interpolation for model reduction of
port-Hamiltonian systems.
48(9):1963--1974, 2012.
[ bib |
DOI ]
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[A349]
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G. Flagg, C. Beattie, and S. Gugercin.
Convergence of the iterative rational Krylov algorithm.
61(6):688--691, 2012.
[ bib |
DOI ]
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[A348]
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S. D. Dukić and A. T. Sarić.
Dynamic model reduction: An overview of available techniques with
application to power systems.
Serbian Journal of Electrical Engineering, 9(2):131--169, 2012.
[ bib |
DOI ]
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[A347]
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M. Drohmann, B. Haasdonk, and M. Ohlberger.
Reduced basis approximation for nonlinear parametrized evolution
equations based on empirical operator interpolation.
34(2):A937--A969, 2012.
[ bib |
DOI ]
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[A346]
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K. K. Chen, J. H. Tu, and R. W. Rowley.
Variants of dynamic mode decomposition: Boundary condition,
Koopman, and Fourier analyses.
Nonlinear Science, 22(6):887--915, 2012.
[ bib |
DOI ]
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[A345]
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R. Castañé-Selga, B. Lohmann, and R. Eid.
Stability preservation in projection-based model order reduction of
large-scale systems.
18(2):122--132, 2012.
[ bib ]
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[A344]
|
P. Benner, B. Kranz, J. Saak, and M. M. Uddin.
Efficient reduced order state space model computation for a class of
second order index one systems.
12(1):699--700, 2012.
[ bib |
DOI ]
|
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[A343]
|
P. Benner, P. Kürschner, and J. Saak.
Improved second-order balanced truncation for symmetric systems.
IFAC Proceedings Volumes (7th Vienna International Conference on
Mathematical Modelling), 45(2):758--762, 2012.
[ bib |
DOI ]
|
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[A342]
|
P. Benner, P. Kürschner, and J. Saak.
A goal-oriented dual LRCF-ADI for balanced truncation.
IFAC Proceedings Volumes (7th Vienna International Conference on
Mathematical Modelling), 45(2):752--757, 2012.
[ bib |
DOI ]
|
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[A341]
|
P. Benner and T. Breiten.
Interpolation-based H2-model reduction of bilinear
control systems.
33(3):859--885, 2012.
[ bib |
DOI ]
|
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[A340]
|
C. A. Beattie, S. Gugercin, and S. Wyatt.
Inexact solves in interpolatory model reduction.
436(8):2916--2943, 2012.
Special Issue dedicated to Danny Sorensen's 65th birthday.
[ bib |
DOI ]
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[A339]
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D. Amsallem and C. Farhat.
Stabilization of projection-based reduced-order models.
Numerical Methods in Engineering, 91(4):358--377, 2012.
[ bib |
DOI ]
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[A338]
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K. Ahuja, E. de Sturler, S. Gugercin, and E. R. Chang.
Recycling BiCG with an application to model reduction.
34(4):A1925--A1949, 2012.
[ bib |
DOI ]
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[A337]
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Y. Xu and T. Zeng.
Optimal H2 model reduction for large scale MIMO
systems via tangential interpolation.
Int. J. Numer. Anal. Model., 8(1):174--188, 2011.
[ bib |
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[A336]
|
T. Wolf, H. Panzer, and B. Lohmann.
Gramian-based error bound in model reduction by Krylov subspace
methods.
IFAC Proceedings Volumes (18th IFAC World Congress),
44(1):3587--3592, 2011.
[ bib |
DOI ]
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[A335]
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J. M. A. Scherpen and A. J. Van Der Schaft.
Balanced model reduction of gradient systems.
Inf. Software Technol., 44(1):12745--12750, 2011.
[ bib |
DOI ]
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[A334]
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T. Reis and J. C. Willems.
A balancing approach to the realization of systems with internal
passivity and reciprocity.
60(1):69--74, 2011.
[ bib |
DOI ]
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[A333]
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T. Reis and T. Stykel.
Lyapunov balancing for passivity-preserving model reduction of RC
circuits.
10(1):1--34, 2011.
[ bib |
DOI ]
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[A332]
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J. Möckel, T. Reis, and T. Stykel.
Linear-quadratic Gaussian balancing for model reduction of
differential-algebraic systems.
84(10):1627--1643, 2011.
[ bib |
DOI ]
|
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[A331]
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T. C. Ionescu, K. Fujimoto, and J. M. A. Scherpen.
Singular value analysis of nonlinear symmetric systems.
56(9):2073--2086, 2011.
[ bib |
DOI ]
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[A330]
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M. Heinkenschloss, T. Reis, and A. C. Antoulas.
Balanced truncation model reduction for systems with inhomogeneous
initial conditions.
47(3):559--564, 2011.
[ bib |
DOI ]
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[A329]
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B. Haasdonk and M. Ohlberger.
Efficient reduced models and a-posteriori error estimation for
parametrized dynamical systems by offline/online decomposition.
17(2):145--161, 2011.
[ bib ]
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[A328]
|
B. Haasdonk, M. Dihlmann, and M. Ohlberger.
A training set and multiple basis generation approach for
parametrized model reduction based on adaptive grids in parameter space.
17(4):423--442, 2011.
[ bib ]
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[A327]
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C. Gu.
QLMOR: A projection-based nonlinear model order reduction approach
using quadratic-linear representation of nonlinear systems.
IEEE Trans. Comput. Aided Des. Integr. Circuits. Syst.,
30(9):1307--1320, 2011.
[ bib |
DOI ]
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[A326]
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F. Freitas, N. Martins, S. L. Varricchio, J. Rommes, and F. C. Veliz.
Reduced-order transfer matrices from RLC network descriptor models
of electric power grids.
26(4):1905--1916, 2011.
[ bib |
DOI ]
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[A325]
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H. Faßbender and A. Soppa.
Machine tool simulation based on reduced order FE models.
Math. Comput. Simulation, 82(3):404--413, 2011.
[ bib ]
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[A324]
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J. L. Eftang, D. J. Knezevic, and A. T. Patera.
An hp certified reduced basis method for parametrized parabolic
partial differential equations.
17(4):395--422, 2011.
[ bib ]
|
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[A323]
|
V. Druskin and V. Simoncini.
Adaptive rational Krylov subspaces for large-scale dynamical
systems.
60(8):546--560, 2011.
[ bib |
DOI ]
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[A322]
|
I. Dones, S. Skogestad, and H. A. Preisig.
Application of balanced truncation to nonlinear systems.
Ind. Eng. Chem. Res., 50(17):10093--10101, 2011.
[ bib |
DOI ]
|
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[A321]
|
P. Benner, Z. Tomljanović, and N. Truhar.
Dimension reduction for damping optimization in linear vibrating
systems.
91(3):179--191, 2011.
[ bib |
DOI ]
|
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[A320]
|
P. Benner and J. Saak.
Efficient balancing-based MOR for large-scale second-order systems.
17(2):123--143, 2011.
[ bib |
DOI ]
|
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[A319]
|
P. Benner and T. Damm.
Lyapunov equations, energy functionals, and model order reduction
of bilinear and stochastic systems.
49(2):686--711, 2011.
[ bib |
DOI ]
|
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[A318]
|
P. Benner, T. Breiten, and T. Damm.
Generalized tangential interpolation for model reduction of
discrete-time MIMO bilinear systems.
84(8):1398--1407, 2011.
[ bib |
DOI ]
|
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[A317]
|
P. Benner and T. Breiten.
On H2-model reduction of linear parameter-varying systems.
11(1):805--806, 2011.
[ bib |
DOI ]
|
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[A316]
|
U. Baur, P. Benner, A. Greiner, J. G. Korvink, J. Lienemann, and C. Moosmann.
Parameter preserving model reduction for MEMS applications.
17(4):297--317, 2011.
[ bib ]
|
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[A315]
|
U. Baur, C. A. Beattie, P. Benner, and S. Gugercin.
Interpolatory projection methods for parameterized model reduction.
33(5):2489--2518, 2011.
[ bib |
DOI ]
|
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[A314]
|
D. Amsallem and C. Farhat.
An online method for interpolating linear parametric reduced-order
models.
33(5):2169--2198, 2011.
[ bib |
DOI ]
|
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[A313]
|
P. Van Dooren, K. A. Gallivan, and P.-A. Absil.
H2-optimal model reduction with higher-order poles.
31(5):2738--2753, 2010.
[ bib |
DOI ]
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[A312]
|
P. J. Schmid.
Dynamic mode decomposition of numerical and experimental data.
J. Fluid Mech., 656:5--28, 2010.
[ bib |
DOI ]
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[A311]
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T. Reis and T. Stykel.
PABTEC: Passivity-preserving balanced truncation for electrical
circuits.
29(9):1354--1367, 2010.
[ bib |
DOI ]
|
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[A310]
|
T. Reis and T. Stykel.
Positive real and bounded real balancing for model reduction of
descriptor systems.
83(1):74--88, 2010.
[ bib |
DOI ]
|
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[A309]
|
H. Panzer, J. Mohring, R. Eid, and B. Lohmann.
Parametric model order reduction by matrix interpolation.
58(8):475--484, 2010.
[ bib ]
|
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[A308]
|
D. J. Knezevic and A. T. Patera.
A certified reduced basis method for the Fokker-Planck equation
of dilute polymeric fluids: FENE dumbbells in extensional flow.
32(2):793--817, 2010.
[ bib ]
|
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[A307]
|
D. B. P. Huynh, D. J. Knezevic, Y. Chen, J. S. Hesthaven, and A. T. Patera.
A natural-norm successive constraint method for inf-sup lower bounds.
199(29):1963--1975, 2010.
[ bib |
DOI ]
|
|
[A306]
|
C. Hartmann, V.-M. Vulcanov, and C. Schütte.
Balanced truncation of linear second-order systems: a Hamiltonian
approach.
Multiscale Model. Simul., 8(4):1348--1367, 2010.
[ bib |
DOI ]
|
|
[A305]
|
J. Fehr and P. Eberhard.
Error-controlled model reduction in flexible multibody dynamics.
J. Comput. Nonlinear Dynam., 5(3):031005--1--031005--8, 2010.
[ bib |
DOI ]
|
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[A304]
|
F. Ebert.
A note on POD model reduction methods for DAEs.
16(2):115--131, 2010.
[ bib ]
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[A303]
|
S. Chaturantabut and D. C. Sorensen.
Nonlinear model reduction via discrete empirical interpolation.
32(5):2737--2764, 2010.
[ bib |
DOI ]
|
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volume 171 of International Series of Numerical Mathematics, pages
393--415. Birkhäuser, Cham, 2021.
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[P76]
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P. Benner, M. Köhler, and J. Saak.
Matrix equations, sparse solvers: M-M.E.S.S.-2.0.1 -- philosophy,
features and application for (parametric) model order reduction.
In P. Benner, T. Breiten, H. Faßbender, M. Hinze, T. Stykel, and
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volume 171 of International Series of Numerical Mathematics, pages
369--392. Birkhäuser, Cham, 2021.
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P. Benner, S. Grundel, and P. Mlinarić.
Clustering-based model order reduction for nonlinear network systems.
In P. Benner, T. Breiten, H. Faßbender, M. Hinze, T. Stykel, and
Z. R., editors, Model Reduction of Complex Dynamical Systems, volume
171 of International Series of Numerical Mathematics, pages 75--96.
Birkhäuser, Cham, 2021.
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[P74]
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P. Benner, P. Goyal, and I. Pontes Duff.
Data-driven identification of Rayleigh-damped second-order systems.
In Realization and Model Reduction of Dynamical Systems -- A
Festschrift in Honor of the 70th Birthday of Thanos Antoulas. Springer,
2020.
accepted April 2020.
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M. Petreczky and I. V. Gosea.
Model reduction and realization theory of linear switched systems.
In Realization and Model Reduction of Dynamical Systems - A
Festschrift in Honor of the 70th Birthday of Thanos Antoulas. Springer,
2020.
accepted July 2020.
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A. Onorati and G. Montenegro.
Control-oriented gas dynamic simulation via model order reduction.
In 1D and Multi-D Modeling Techniques for IC Engine
Simulation, pages 221--255. SAE, 2020.
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[P71]
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C. Gräßle, M. Hinze, and S. Volkwein.
Model order reduction by proper orthogonal decomposition.
In P. Benner, S. Grivet-Talocia, A. Quarteroni, G. Rozza, W. H. A.
Schilders, and L. M. Silveira, editors, Model Order Reduction.
Volume 2: Snapshot-Based Methods and Algorithms. De Gruyter,
Berlin, Boston, 2020.
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[P70]
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I. V. Gosea, I. Pontes Duff, P. Benner, and A. C. Antoulas.
Model order reduction of switched linear systems with constrained
switching.
In IUTAM Symposium on Model Order Reduction of Coupled Systems,
Stuttgart, Germany, May 22--25, 2018, volume 36 of IUTAM Bookseries,
pages 41--53. Springer, Cham, Switzerland, 2020.
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[P69]
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P. Benner and S. W. R. Werner.
MORLAB -- A model order reduction framework in MATLAB and
Octave.
In A. M. Bigatti, J. Carette, J. H. Davenport, M. Joswig, and
T. de Wolff, editors, Mathematical Software -- ICMS 2020, volume 12097,
pages 432--441. Springer International Publishing, Cham, 2020.
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[P68]
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A. C. Antoulas, I. V. Gosea, and M. Heinkenschloss.
Data-driven model reduction for a class od semi-explicit DAEs using
the Loewner framework.
In S. Grundel, T. Reis, and S. Schöps, editors, Progress
in Differential-Algebraic Equations II, Differential-Algebraic Equations
Forum, pages 185--210. Springer, 2020.
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[P67]
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Y. Yue, L. Feng, P. Benner, R. Pulch, and S. Schöps.
Reduced models and uncertainty quantification.
In Nanoelectronic Coupled Problems Solutions, volume 29 of
Mathematics in Industry book series (MATHINDUSTRY) and The European
Consortium for Mathematics in Industry book sub series (TECMI), pages
329--346. Springer, 2019.
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[P66]
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P. Schulze, J. Reiss, and V. Mehrmann.
Model reduction for a pulsed detonation combuster via shifted proper
orthogonal decomposition.
In R. King, editor, Active Flow and Combustion Control 2018,
volume 141 of Notes on Numerical Fluid Mechanics and Multidisciplinary
Design, pages 271--286. Springer, Cham, 2019.
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S. Grundel, P. Sauerteig, and K. Worthmann.
Surrogate models for coupled microgrids.
In Progress in Industrial Mathematics at ECMI 2018, pages
477--483. Springer, 2019.
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[P64]
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L. Feng and P. Benner.
Parametric model order reduction for electro-thermal coupled
problems.
In Nanoelectronic Coupled Problems Solutions, volume 29 of
Mathematics in Industry book series (MATHINDUSTRY) and The European
Consortium for Mathematics in Industry book sub series (TECMI), pages
293--309. Springer, 2019.
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[P63]
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P. Benner and H. Faßbender.
Model order reduction: Techniques and tools.
In J. Baillieul and T. Samad, editors, Encyclopedia of Systems
and Control. Springer, 2019.
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[P62]
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N. Banagaaya, L. Feng, and P. Benner.
Sparse (P)MOR for electro-thermal coupled problems with many
inputs.
In Nanoelectronic Coupled Problems Solutions, volume 29 of
Mathematics in Industry, pages 311--328. Springer, 2019.
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[P61]
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A. C. Antoulas, I. V. Gosea, and M. Heinkenschloss.
On the Loewner framework for model reduction of Burgers'
equation.
In R. King, editor, Active Flow and Combustion Control, Notes
on Numerical Fluid Mechanics and Multidisciplinary Design, pages 255--270.
Springer, Cham, Switzerland, 2019.
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D. Hartmann, M. Herz, and U. Wever.
Model order reduction a key technology for digital twins.
In W. Keiper, A. Milde, and S. Volkwein, editors, Reduced-Order
Modeling (ROM) for Simulation and Optimization, pages 167--179. Springer,
Cham, 2018.
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J. Fehr, D. Grunert, P. Holzwarth, B. Fröhlich, N. Walker, and
P. Eberhard.
Morembs---a model order reduction package for elastic multibody
systems and beyond.
In Reduced-Order Modeling (ROM) for Simulation and Optimization:
Powerful Algorithms as Key Enablers for Scientific Computing, pages
141--166. Springer International Publishing, Cham, 2018.
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[P58]
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P. Benner and P. Goyal.
An iterative model reduction scheme for quadratic-bilinear descriptor
systems with an application to Navier-Stokes equations.
In W. Keiper, A. Milde, and S. Volkwein, editors, Reduced-Order
Modeling for Simulation and Optimization, pages 1--19. Springer, 2018.
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[P57]
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P. Benner, M. Braukmüller, and S. Grundel.
A direct index 1 DAE model of gas networks.
In W. Keiper, A. Milde, and S. Volkwein, editors, Reduced-Order
Modeling (ROM) for Simulation and Optimization, pages 99--119. Springer,
Cham, 2018.
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[P56]
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P. Benner and T. Stykel.
Model order reduction for differential-algebraic equations: A survey.
In A. Ilchmann and T. Reis, editors, Surveys in
Differential-Algebraic Equations IV, Differential-Algebraic Equations Forum,
pages 107--160. Springer International Publishing, Cham, Mar. 2017.
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[P55]
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M. Uzunca and B. Karasözen.
Energy stable model order reduction for the Allen-Cahn equation.
In P. Benner, M. Ohlberger, A. T. Patera, G. Rozza, and K. Urban,
editors, Model Reduction of Parametrized Systems, volume 17 of
Modeling, Simulation and Applications, pages 403--419. Springer, Cham, 2017.
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[P54]
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A. Steinbrecher and T. Stykel.
Element-based model reduction in circuit simulation.
In P. Benner, editor, System Reduction for Nanoscale IC
Design, volume 20 of Mathematics in Industry, pages 39--85. Springer,
Cham, 2017.
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[P53]
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O. Schmidt, M. Hauser, and P. Lang.
Coupling of numeric/symbolic reduction methods for generating
parametrized models of nanoelectronic systems.
In P. Benner, editor, System Reduction for Nanoscale IC
Design, volume 20 of Mathematics in Industry, pages 136--156.
Springer, Cham, 2017.
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[P52]
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Y. Lu, M. Marheineke, and J. Mohring.
Interpolation strategy for BT-based parametric MOR of gas
pipeline-networks.
In P. Benner, M. Ohlberger, A. Patera, R. G., and K. Urban, editors,
Model Reduction of Parametrized Systems, volume 17 of MS & A,
pages 387--401. Springer, 2017.
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B. Liljegren-Sailer and M. Marheineke.
A structure-preserving model order reduction approach for
space-discrete gas networks with active elements.
In P. Quintela, P. Barral, D. Gómez, F. J. Pena,
J. Rodríguez, P. Salgado, and M. E. Vázquez-Méndez, editors,
Progress in Industrial Mathematics at ECMI 2016, volume 26 of
Mathematics in Industry, pages 439--446. Springer, 2017.
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[P50]
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M. Hinze, M. Kunkel, U. Matthes, and M. Vierling.
Model order reduction of integrated circuits in electrical networks.
In P. Benner, editor, System Reduction for Nanoscale IC
Design, volume 20 of Mathematics in Industry, pages 1--37. Springer,
Cham, 2017.
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C. Himpe and M. Ohlberger.
Cross-Gramian-based model reduction: A comparison.
In P. Benner, M. Ohlberger, A. Patera, G. Rozza, and K. Urban,
editors, Model Reduction of Parametrized Systems, volume 17 of
Modeling, Simulation and Applications, pages 271--283. Springer, Cham, 2017.
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[P48]
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B. Haasdonk.
Reduced basis methods for parametrized PDEs---a tutorial
introduction for stationary and instationary problems.
In P. Benner, A. Cohen, M. Ohlberger, and K. Willcox, editors,
Model Reduction and Approximation: Theory and Algorithms, pages 65--136.
SIAM, 2017.
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F. Chinesta, A. Huerta, G. Rozza, and K. Willcox.
Model reduction methods.
In Encyclopedia of Computational Mechanics, volume 1, pages
1--36. 2. edition, 2017.
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M. Bollhöfer and A. K. Eppler.
Low-rank Cholesky factor Krylov subspace methods for generalized
projected Lyapunov equations.
In P. Benner, editor, System Reduction for Nanoscale IC
Design, volume 20 of Mathematics in Industry, pages 157--193.
Springer, Cham, 2017.
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[P45]
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P. Benner and A. Schneider.
Reduced representation of power grid models.
In P. Benner, editor, System Reduction for Nanoscale IC
Design, volume 20 of Mathematics in Industry, pages 87--134. Springer,
Cham, 2017.
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P. Benner, P. Goyal, and M. Redmann.
Truncated Gramians for bilinear systems and their advantages in
model order reduction.
In P. Benner, M. Ohlberger, T. Patera, G. Rozza, and K. Urban,
editors, Model Reduction of Parametrized Systems, volume 17 of
MS&A - Modeling, Simulation and Applications, pages 285--300. Springer,
Cham, 2017.
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[P43]
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P. Benner and T. Breiten.
Model order reduction based on system balancing.
In P. Benner, A. Cohen, M. Ohlberger, and K. Willcox, editors,
Model Reduction and Approximation, Computational Science & Engineering,
pages 261--295. SIAM, Philadelphia, PA, 2017.
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[P42]
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U. Baur, P. Benner, B. Haasdonk, C. Himpe, I. Martini, and M. Ohlberger.
Comparison of methods for parametric model order reduction of
time-dependent problems.
In P. Benner, A. Cohen, M. Ohlberger, and K. Willcox, editors,
Model Reduction and Approximation: Theory and Algorithms, pages 377--407.
SIAM, 2017.
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[P41]
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Y. Lu, M. Marheineke, and J. Mohring.
Stability-preserving interpolation strategy for parametric MOR of
gas pipeline-networks.
In P. Quintela, P. Barral, D. Gómez, F. J. Pena,
J. Rodríguez, P. Salgado, and M. E. Vázquez-Méndez, editors,
Progress in Industrial Mathematics at ECMI 2016, volume 26 of
Mathematics in Industry, pages 431--437. Springer, 2016.
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B. Karasözen, M. Uzunca, and T. Küçükseyhan.
Model order reduction for pattern formation in FitzHugh-Nagumo
equations.
In B. Karasözen, M. Manguoğlu, M. Tezer-Sezgin,
S. Göktepe, and ö. Uğur, editors, Numerical
Mathematics and Advanced Applications ENUMATH 2015, pages 369--377. Springer
International Publishing, Cham, 2016.
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L. Iapichino, S. Trenz, and S. Volkwein.
Reduced-order multiobjective optimal control of semilinear parabolic
problems.
In B. Karasözen, M. Manguoğlu, M. Tezer-Sezgin,
S. Göktepe, and ö. Uğur, editors, Numerical Mathematics
and Advanced Applications ENUMATH 2015, pages 389--397. Springer
International Publishing, Cham, 2016.
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[P38]
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S. Grundel, N. Hornung, and S. Roggendorf.
Numerical aspects of model order reduction for gas transportation
networks.
In S. Koziel, L. Leifsson, and X.-S. Yang, editors,
Simulation-Driven Modeling and Optimization, pages 1--28. Springer, 2016.
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[P37]
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N. Lang, J. Saak, and P. Benner.
Model order reduction for thermo-elastic assembly group models.
In K. Großmann, editor, Thermo Energetic Design of Machine
Tools, chapter 8, pages 85--92. Springer International Publishing
Switzerland, 2015.
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A. Galant, K. Großmann, and A. Mühl.
Thermo-elastic simulation of entire machine tool.
In K. Großmann, editor, Thermo Energetic Design of Machine
Tools, chapter 8, pages 69--84. Springer International Publishing
Switzerland, 2015.
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[P35]
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P. Benner and J. Heiland.
LQG-balanced truncation low-order controller for stabilization of
laminar flows.
In R. King, editor, Active Flow and Combustion Control 2014,
volume 127 of Notes on Numerical Fluid Mechanics and Multidisciplinary
Design, pages 365--379. Springer International Publishing, 2015.
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[P34]
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P. Benner, T. Breiten, and L. Feng.
Matrix equations and model reduction.
In Matrix Functions and Matrix Equations, volume 19 of
Series in Contemporary Applied Mathematics, pages 50--57. World Scientific,
2015.
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[P33]
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M. Baumann, J. Heiland, and M. Schmidt.
Discrete input/output maps and their relation to Proper
Orthogonal Decomposition.
In P. Benner, M. Bollhöfer, D. Kressner, C. Mehl, and
T. Stykel, editors, Numerical Algebra, Matrix Theory,
Differential-Algebraic Equations and Control Theory, pages 585--608.
Springer, Cham, 2015.
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[P32]
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S. Grundel, L. Jansen, N. Hornung, T. Clees, C. Tischendorf, and P. Benner.
Model order reduction of differential algebraic equations arising
from the simulation of gas transport networks.
In Progress in Differential-Algebraic Equations,
Differential-Algebraic Equations Forum, pages 183--205. Springer Berlin
Heidelberg, 2014.
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[P31]
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L. Feng and P. Benner.
A robust algorithm for parametric model order reduction based on
implicit moment matching.
In A. Quarteroni and G. Rozza, editors, Reduced Order Methods
for modeling and computational reduction, volume 9 of MS & A, pages
159--186. Berlin, Heidelberg, New York, 2014.
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[P30]
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P. Benner, E. Sachs, and S. Volkwein.
Model order reduction for PDE constrained optimization.
In G. Leugering, P. Benner, S. Engell, A. Griewank, H. Harbrecht,
M. Hinze, R. Rannacher, and S. Ulbrich, editors, Trends in PDE
Constrained Optimization, volume 165 of International Series of
Numerical Mathematics, pages 303--326. Birkhäuser, Basel, 2014.
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[P29]
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P. Benner, E. Dufrechou, P. Ezzatti, P. Igounet, E. S. Quintana-Ortí,
and A. Remón.
Accelerating band linear algebra operations on GPUs with
application in model reduction.
In B. Murgante, S. Misra, A. M. A. C. Rocha, C. M. Torre, J. G.
Rocha, M. I. Falcão, D. Taniar, B. O. Apduhan, and O. Gervasi, editors,
Computational Science and Its Applications - ICCSA 2014 - 14th
International Conference, Guimarães, Portugal, 2014, Proceedings, Part
VI, volume 8584, pages 386--400. 2014.
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M. Geußand K. J. Diepold.
An approach for stability-preserving model order reduction for
switched linear systems based on individual subspaces.
In G. Roppenecker and B. Lohmann, editors, Methoden und
Anwendungen der Regelungstechnik. Shaker Verlag, Aachen, Sept. 2013.
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M. Köhler and J. Saak.
A shared memory parallel implementation of the IRKA algorithm for
H2 model order reduction.
In P. Manninen and P. öster, editors, Applied Parallel and
Scientific Computing, volume 7782, pages 541--544. Berlin/Heidelberg, 2013.
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[P26]
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A. Hochman, D. M. Vasilyev, M. J. Rewieński, and J. K. White.
Projection-based nonlinear model order reduction.
In T. Bechtold, G. Schrag, and L. Feng, editors, System-Level
Modeling of MEMS, Advanced Micro & Nanosystems. 2013.
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[P25]
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S. Grundel, N. Hornung, B. Klaassen, P. Benner, and T. Clees.
Computing surrogates for gas network simulation using model order
reduction.
In S. Koziel and L. Leifsson, editors, Surrogate-Based Modeling
and Optimization, pages 189--212. Springer, New York, 2013.
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L. Feng, P. Benner, and J. G. Korvink.
System-Level Modeling of MEMS by Means of Model Order Reduction
(Mathematical Approximation) - Mathematical Background.
In T. Bechtold, G. Schrag, and L. Feng, editors, System-level
Modeling of MEMS, volume 10 of Advanced Micro and Nanosystems, pages
53--93. Wiley-VCH, Weinheim, 2013.
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[P23]
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P. Benner and A. Schneider.
Some remarks on a priori error estimation for ESVDMOR.
In M. Bastiaan and J.-R. Poirier, editors, Scientific Computing
in Electrical Engineering SCEE 2010, volume 16 of Mathematics in
Industry / The European Consortium for Mathematics in Industry, pages
15--24. Springer-Verlag, Berlin, 2012.
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[P22]
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P. Benner, E. P., Q. E. S., and A. Remón.
Accelerating BST methods for model reduction with graphics
processors.
In R. Wyrzykowski, J. Dongarra, K. Karczewski, and J. Wasniewski,
editors, Parallel Processing and Applied Mathematics - 9th International
Conference, PPAM 2011, Torun, Poland, September 11-14, 2011. Revised
Selected Papers, Part I, volume 7203, pages 549--558. Berlin/Heidelberg,
2012.
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[P21]
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P. Benner, P. Ezzatti, D. Kressner, E. S. Quintana-Ortí, and A. Remón.
Accelerating model reduction of large linear systems with graphics
processors.
In K. Jónasson, editor, Applied Parallel and Scientific
Computing, volume 7134, pages 88--97. Berlin/Heidelberg, 2012.
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[P20]
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P. Benner and A. Schneider.
On stability, passivity and reciprocity preservation of ESVDMOR.
In P. Benner, M. Hinze, and E. J. W. ter Maten, editors, Model
Reduction for Circuit Simulation, volume 74, pages 277--288. 2011.
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[P19]
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A. C. Antoulas, C. A. Beattie, and S. Gugercin.
Interpolatory model reduction of large-scale dynamical systems.
In J. Mohammadpour and K. M. Grigoriadis, editors, Efficient
Modeling and Control of Large-Scale Systems, pages 3--58. Springer US, 2010.
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T. C. Ionescu and J. M. A. Scherpen.
Nonlinear cross gramians.
In System Modeling and Optimization, volume 312 of IFIP
Advances in Information and Communication Technology, pages 293--306.
Springer, 2009.
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A. Vandendorpe and P. Van Dooren.
Model reduction of interconnected systems.
In W. H. A. Schilders, H. A. van der Vorst, and J. Rommes, editors,
Model Order Reduction: Theory, Research Aspects and Applications,
volume 13 of Mathematics in Industry, pages 305--321. Springer, Berlin,
Heidelberg, 2008.
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T. Reis and T. Stykel.
A survey on model reduction of coupled systems.
In W. H. A. Schilders, H. A. van der Vorst, and J. Rommes, editors,
Model Order Reduction: Theory, Research Aspects and Applications, pages
133--155. Springer Berlin Heidelberg, Berlin, Heidelberg, 2008.
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[P15]
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J. M. Badía, P. Benner, R. Mayo, E. S. Quintana-Ortí,
G. Quintana-Ortí, and A. Remón.
Parallel implementation of LQG balanced truncation for large-scale
systems.
In I. Lirkov, S. Margenov, and J. Wasniewski, editors,
Large-Scale Scientific Computing, volume 4818, pages 227--234. Springer
Berlin Heidelberg, 2008.
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R.-C. Li and Z. Bai.
Structure-preserving model reduction.
In J. Dongarra, K. Madsen, and J. Waśniewski, editors,
Applied Parallel Computing. State of the Art in Scientific Computing: 7th
International Workshop, PARA 2004, Lyngby, Denmark, June 20-23, 2004. Revised
Selected Papers, pages 323--332. Springer Berlin Heidelberg, 2006.
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E. B. Rudnyi and J. G. Korvink.
Boundary condition independent thermal model.
In P. Benner, D. C. Sorensen, and V. Mehrmann, editors,
Dimension Reduction of Large-Scale Systems, volume 45 of Lecture Notes
in Computational Science and Engineering, pages 345--348. Springer Berlin
Heidelberg, 2005.
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J. G. Korvink and E. B. Rudnyi.
Oberwolfach benchmark collection.
In P. Benner, D. C. Sorensen, and V. Mehrmann, editors,
Dimension Reduction of Large-Scale Systems, volume 45, pages 311--315.
Springer Berlin Heidelberg, 2005.
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Z. Bai, P. M. Dewilde, and R. W. Freund.
Reduced-order modeling.
In Handbook of numerical analysis. Vol. XIII, Handb. Numer.
Anal., XIII, pages 825--891. Amsterdam, 2005.
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[P10]
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P. Benner, R. Mayo, E. S. Quintana, and G. Quintana-Ortí.
A model reduction web environment for very large linear dynamical
systems.
In V. Y. Pan and L. T. Yang, editors, Parallel and Distributed
Scientific and Engineering Computing: Practice and Experience, volume 15 of
Advances in Computation: Theory and Practice, pages 23--33. NOVA
Science Publishers, Hauppauge, NY, 2004.
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M. Fahl and E. W. Sachs.
Reduced order modelling approaches to PDE-constrained optimization
based on proper orthogonal decomposition.
In Large-scale PDE-constrained optimization (Santa Fe,
NM, 2001), volume 30 of Lect. Notes Comput. Sci. Eng., pages
268--280. Springer, Berlin, 2003.
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P. Benner, R. Mayo, E. S. Quintana, and G. Quintana-Ortí.
Enhanced services for remote model reduction of large-scale dense
linear systems.
In J. Fagerholm, J. Haataja, J. Järvinen, M. Lyly, P. Raback,
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A. Varga.
Model reduction software in the SLICOT library.
In B. N. Datta, editor, Applied and Computational Control,
Signals, and Circuits, volume 629 of The Kluwer International Series in
Engineering and Computer Science, pages 239--282. Kluwer Academic
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K. Afanasiev and M. Hinze.
Adaptive control of a wake flow using proper orthogonal
decomposition.
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P. Benner, E. S. Quintana-Ortí, and G. Quintana-Ortí.
Singular perturbation approximation of large, dense linear systems.
In Proc. 2000 IEEE Intl. Symp. CACSD, Anchorage, Alaska, USA,
September 25--27, 2000, pages 255--260. IEEE Press, Piscataway, NJ, 2000.
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R. W. Freund.
Reduced-order modeling techniques based on Krylov subspaces and
their use in circuit simulation.
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W. Lang and U. Lezius.
Numerical realization of the balanced reduction of a control problem.
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A. Varga.
Minimal realization procedures based on balancing and related
techniques.
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K. Glover and J. R. Partington.
Bounds on the achievable accuracy in model reduction.
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[P289]
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L. Peterson, P. Goyal, , I. V. Gosea, P. Benner, and K. Sundmacher.
Reduced-order modeling for a methanation reactor by harnessing the
effectiveness of nonlinear decoders.
In 7th International Workshop on Model Order Reduction
Techniques (MORTech 2025), Zaragoza, Spain, November 26 - 28, 2025.
extended abstract, accepted for presentation.
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[P288]
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L. Peterson, M. Büttner, A. Forootani, I. V. Gosea, P. Benner, and
K. Sundmacher.
Greedy sampling neural network SINDy with control for a catalytic
co2 methanation reactor.
Springer, 2025.
full paper, presented at the 15th International Conference on
Large-Scale Scientific Computations (LSSC), Sozopol, Bulgaria, June 16 - 20.
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[P287]
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P. Hickisch, J. Saak, D. Hohlfeld, and T. Bechtold.
Two-step model order reduction for a thermal finite element model of
a power electronics module.
In 11th Vienna International Conference on Mathematical
Modelling MATHMOD 2025, volume 59, pages 379--384, Vienna, Austria, 2025.
11th Vienna International Conference on Mathematical Modelling
MATHMOD 2025.
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[P286]
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J. Heiland, I. V. Gosea, U. Römer, D. Pradovera, H. Sreekumar, and
S. Langer.
Adaptive rational interpolation and higher-order SVD for low-rank
tensor approximation in structural dynamics simulations.
In 23rd European Control Conference (ECC), June 24--27,
Thessaloniki, Greece, pages 3213--3218, 2025.
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A. Carlucci, I. V. Gosea, and S. Grivet-Talocia.
An extension of vector fitting to weakly nonlinear circuits.
In IEEE 29th Workshop on Signal and Power Integrity (SPI),
Gaeta, Italy, May 11--14, pages 1--4, 2025.
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[P284]
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C. Wang, L. Feng, W. Lu, W. Bian, Z. You, and P. Benner.
Active learning enhanced deep-learning surrogate model for fast
MEMS design with high-dimensional design parameter spaces.
In 19th IEEE International Conference on Nano/Micro Engineered
and Molecular Systems (IEEE NEMS 2024), 2024.
accepted.
[ bib ]
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[P283]
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D. S. Karachalios, I. V. Gosea, K. Kour, and A. C. Antoulas.
Bilinear realization from I/O data with NNs.
In M. van Beurden, N. Budko, G. Ciuprina, W. Schilders, H. Bansal,
and R. Barbulescu, editors, Scientific Computing in Electrical
Engineering SCEE 2022, volume 43 of Mathematics in Industry, pages
184--192. Springer, Cham, 2024.
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[P282]
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I. V. Gosea and J. Heiland.
Implicit and explicit matching of non-proper transfer functions in
the Loewner framework.
In 2024 European Control Conference (ECC), pages 3452--3457,
2024.
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[P281]
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T. Bradde, I. V. Gosea, and S. Grivet-Talocia.
Fast macromodeling of large-scale multiports with guaranteed
stability.
In 2024 IEEE International Symposium on Electromagnetic
Compatibility, Signal & Power Integrity (EMC+SIPI), pages 472--477, 2024.
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[P280]
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V. Bansal, L. Feng, V. de la Rubia, and P. Benner.
Parametric s-parameter prediction using deep learning.
In Proc. 33rd Conference on Electrical Performance of Electronic
Packaging and Systems (EPEPS 2024), pages 1--4. IEEE, 2024.
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[P279]
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A. Zuyev and I. V. Gosea.
Approximating a flexible beam model in the Loewner framework.
In 21st European Control Conference (ECC), pages 1--7, 2023.
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[P278]
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S. Monem Abdelhafez, P. Benner, and C. Lessig.
Improved projective dynamics global using snapshots-based reduced
bases.
In ACM SIGGRAPH 2023 Posters, SIGGRAPH '23, New York, NY,
USA, 2023. Association for Computing Machinery.
[ bib |
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[P277]
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E. Mattucci., L. Feng, P. Benner, D. Romano, and G. Antonini.
Fast frequency-domain analysis for parametric electromagnetic models
using deep learning.
In IEEE 32nd Conference on Electrical Performance of Electronic
Packaging and Systems (EPEPS). IEEE, 2023.
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[P276]
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I. V. Gosea, L. A. Zivkovic, D. S. Karachalios, V.-K. T., and A. C. Antoulas.
A data-driven nonlinear frequency response approach based on the
Loewner framework: preliminary analysis.
In IFAC-PapersOnLine, 12th Symposium on Nonlinear Control
Systems NOLCOS 2022 Canberra, Australia, January 4-6, 2023, volume 56, pages
234--239. Elsevier, 2023.
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[P275]
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Q. Aumann, P. Benner, J. Saak, and J. Vettermann.
Model order reduction strategies for the computation of compact
machine tool models.
In 3rd International Conference on Thermal Issues in Machine
Tools (ICTIMT2023), pages 132--145, Cham, 2023. Springer International
Publishing.
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[P274]
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S. W. R. Werner, I. V. Gosea, and S. Gugercin.
Structured vector fitting framework for mechanical systems.
In IFAC-PapersOnLine, 18th Vienna International Conference on
Mathematical Modelling MATHMOD 2022, volume 55, pages 163--168, 2022.
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[P273]
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D. S. Karachalios, I. V. Gosea, and A. C. Antoulas.
A framework for fitting quadratic-bilinear systems with applications
to models of electrical circuits.
In IFAC-PapersOnLine, 18th Vienna International Conference on
Mathematical Modelling MATHMOD 2022, volume 55, pages 7--12. Elsevier, 2022.
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[P272]
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I. V. Gosea and I. Pontes Duff.
An iterative realization-free approach for model reduction of
bilinear systems via Hermitian interpolation.
In 20th European Control Conference (ECC), pages 584--589,
2022.
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[P271]
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M. H. Mahmoudi and S. Grundel.
Estimation of time-dependent parameters in a simple compartment model
using Covid 19 data.
In 21st ECMI Conference on Industrial and Applied Mathematics,
2021.
Submitted.
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[P270]
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C. Himpe, S. Grundel, and P. Benner.
Next-gen gas network simulation.
In Progress in Industrial Mathematics at ECMI 2021, page
(Accepted), 2021.
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[P269]
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P. Goyal and P. Benner.
Learning dynamics from noisy measurements using deep learning with a
Runge-Kutta constraint.
In Proc. The Symbiosis of Deep Learning and Differential
Equations - NeurIPS, 2021.
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[P268]
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I. V. Gosea, Q. Zhang, and A. C. Antoulas.
Data-driven modeling from noisy measurements.
In Special Issue: 7th GAMM Juniors' Summer School on Applied
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I. V. Gosea, C. Poussot-Vassal, and A. C. Antoulas.
On enforcing stability for data-driven reduced-order models.
In 29th Mediterranean Conference on Control and Automation
(MED), Virtual, pages 487--493, 2021.
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[P266]
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I. V. Gosea, M. Petreczky, and A. C. Antoulas.
Reduced-order modeling of LPV systems in the Loewner framework.
In 60th IEEE Conference on Decision and Control (CDC),
December 14--17, Austin, TX, USA, pages 3299--3305, 2021.
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I. V. Gosea, D. S. Karachalios, and A. C. Antoulas.
Learning reduced-order models of quadratic control systems from
input-output data.
In Proc. European Control Conf. 2021, Delft, Netherlands, pages
1426--1431. IEEE, 2021.
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[P264]
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I. V. Gosea, D. Karachalios, and A. C. Antoulas.
Reduced-order modeling of block-oriented nonlinear systems from data.
In 24th International Symposium on Mathematical Theory of
Networks and Systems (MTNS), Cambridge, UK, August 23--27, 2021.
extended abstract, accepted for publication.
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[P263]
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I. V. Gosea, S. Gugercin, and B. Unger.
Parametric model reduction via rational interpolation along
parameters.
In 60th IEEE Conference on Decision and Control (CDC),
December 14--17, Austin, TX, USA, pages 6895--6900, 2021.
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[P262]
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L. Feng, L. Lombardi, G. Antonini, and P. Benner.
Stable macromodels for delayed PEEC models with error estimation.
In 2021 International Applied Computational Electromagnetics
Society (ACES) Symposium ACES2021, August 1--5, 2021, Online-Live,
Interactive, pages 1--4. IEEE, 2021.
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[P261]
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J. Bremer, J. Heiland, P. Benner, and K. Sundmacher.
Non-intrusive time Galerkin POD for optimal control of a
fixed-bed reactor for CO2 methanation.
In 11th International Symposium on Advanced Control of Chemical
Processes (ADCHEM), volume 54 of IFAC-PapersOnLine, pages 122--127,
2021.
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[P260]
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I. V. Gosea, M. Petreczky, J. Leth, R. Wisniewski, and A. C. Antoulas.
Model reduction of linear hybrid systems.
In 59th IEEE Conference on Decision and Control (CDC),
December 14--18, Jeju Island, Republic of Korea, pages 110--117, 2020.
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[P259]
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I. V. Gosea, M. Petreczky, C. Fiter, and A. C. Antoulas.
Balanced truncation for linear switched systems.
In Book of Abstracts of XXI Householder Symposium on Numerical
Linear Algebra, Selva di Fasano, Italy, June 14--19, 2020.
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[P258]
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I. V. Gosea, D. Karachalios, and A. C. Antoulas.
Modeling in the Loewner framework: from linear dynamics to
quadratic nonlinearities.
In 21st IFAC World Congress, Berlin, Germany, July 12--17,
2020.
accepted for publication.
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[P257]
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I. V. Gosea and S. Gugercin.
The AAA framework for modeling linear dynamical systems with
quadratic output.
In 21st IFAC World Congress, Berlin, Germany, July 12--17,
2020.
extended abstract, accepted for publication.
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[P256]
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N. Banagaaya, S. Grundel, and P. Benner.
Index-aware MOR for gas transport networks with many supply inputs.
In IUTAM Symposium on Model Order Reduction of Coupled
Systems, volume 36 of IUTAM Bookseries, pages 191--207, 2020.
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[P255]
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A. C. Antoulas, I. V. Gosea, and M. Heinkenschloss.
Reduction of systems with polynomial nonlinearities in the Loewner
framework.
In Book of Abstracts of XXI Householder Symposium on Numerical
Linear Algebra, Selva di Fasano, Italy, June 14--19, 2020.
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W. Zhang and M. Wei.
Solving generalized eigenvalue problem: an alternative approach for
dynamic mode decomposition.
In AIAA Scitech 2019 Forum, page 1897, 2019.
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[P253]
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Y. Yue, L. Feng, and P. Benner.
An adaptive method for interpolating reduced-order models based on
matching and continuation of poles.
In 2019 IEEE MTT-S International Conference on Numerical
Electromagnetic and Multiphysics Modeling and Optimization (NEMO 2019),
2019.
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[P252]
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J. Leung, M. Kinnaert, J.-C. Maun, and F. Villella.
Model reduction of coherent LPV models in power systems.
In 2019 IEEE Power & Energy Society General Meeting (PESGM),
page 19302246, 2019.
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[P251]
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J. Leung, M. Kinnaert, J.-C. Maun, and F. Villella.
LPV modeling of clusters in dynamic power system models.
In 2019 IEEE Milan PowerTech, page 18938976, 2019.
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[P250]
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I. V. Gosea, I. Pontes Duff, P. Benner, and A. C. Antoulas.
Model order reduction of bilinear time-delay systems.
In 18th European Control Conference (ECC), pages 2289--2294,
2019.
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[P249]
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I. V. Gosea and A. C. Antoulas.
A note on modeling some classes of nonlinear systems from data.
In 15th International Conference on Computational Plasticity
(COMPLAS), September 3--5, Barcelona, Spain, pages 145--156, 2019.
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[P248]
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I. V. Gosea and A. C. Antoulas.
A two-sided iterative framework for model reduction of linear systems
with quadratic output.
In 58th IEEE Conference on Decision and Control (CDC),
December 11--13, Nice, France, pages 7812--7817, 2019.
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[P247]
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L. Feng and P. Benner.
Efficient error estimator for model order reduction of linear
parametric systems.
In 2019 IEEE/MTT-S International Microwave Symposium (IMS
2019), pages 346--349. IEEE, 2019.
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[P246]
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P. Benner and S. W. R. Werner.
Frequenz- und zeitbeschränktes balanciertes Abschneiden
für Systeme zweiter Ordnung.
In T. Meurer and F. Woittennek, editors, Tagungsband GMA-FA 1.30
'Modellbildung, Identifikation und Simulation in der Automatisierungstechnik'
und GMA-FA 1.40 'Systemtheorie und Regelungstechnik', Workshops in Anif,
Salzburg, 23.-27.09.2019, pages 460--474, 2019.
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[P245]
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P. Benner and S. W. R. Werner.
MORLAB -- Model Order Reduction LABoratory.
In T. Meurer and F. Woittennek, editors, Tagungsband GMA-FA 1.30
'Modellbildung, Identifikation und Simulation in der Automatisierungstechnik'
und GMA-FA 1.40 'Systemtheorie und Regelungstechnik', Workshops in Anif,
Salzburg, 23.-27.09.2019, pages 337--342, 2019.
[ bib |
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[P244]
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N. Banagaaya, P. Benner, and S. Grundel.
Index-preserving model order reduction for differential-algebraic
systems arising in gas transport networks.
In Progress in Industrial Mathematics at ECMI 2018, volume 30
of Mathematics in Industry, pages 291--297, 2019.
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[P243]
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Y. Yue, L. Feng, and P. Benner.
Interpolation of reduced-order models based on modal analysis.
In 2018 IEEE MTT-S International Conference on Numerical
Electromagnetic and Multiphysics Modeling and Optimization (NEMO 2018),
2018.
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[P242]
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D. Osipov, N. Duan, A. Dimitrovski, S. Allu, S. Simunovic, and K. Sun.
Adaptive model reduction for Parareal in time method for transient
stability simulations.
In 2018 IEEE Power & Energy Society General Meeting (PESGM),
2018.
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DOI ]
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[P241]
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P. Mlinarić, T. Ishizaki, A. Chakrabortty, S. Grundel, P. Benner, and J.-i.
Imura.
Synchronization and aggregation of nonlinear power systems with
consideration of bus network structures.
In 2018 European Control Conference (ECC), pages 2266--2271,
2018.
[ bib |
DOI ]
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[P240]
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C. Himpe, T. Leibner, and S. Rave.
HAPOD - fast, simple and reliable distributed POD computation.
In ARGESIM Report (MATHMOD 2018 Extended Abstract Volume),
volume 55, pages 119--120, 2018.
[ bib |
DOI ]
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[P239]
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I. V. Gosea and A. C. Antoulas.
On the H2 norm and iterative model order reduction of
linear switched systems.
In 18th European Control Conference (ECC), June 12--15,
Limassol, Cyprus, pages 2983--2988, 2018.
[ bib |
DOI ]
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[P238]
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P. Benner, R. Herzog, N. Lang, I. Riedel, and J. Saak.
Optimal sensor placement based on model order reduction.
In Proceedings of the CIRP sponsored Conference on Thermal
Issues in Machine Tools, pages 355--365, 2018.
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[P237]
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P. Benner, S. Grundel, and C. Himpe.
Parametric model order reduction for gas flow models.
In ScienceOpen Posters (MoRePaS 2018 - Model Reduction of
Parametrized Systems IV), 2018.
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[P236]
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N. Banagaaya, L. Feng, W. Schoenmaker, P. Meuris, R. Gillon, and P. Benner.
Sparse model order reduction for electro-thermal problems with many
inputs.
In U. Langer, W. Amrhein, and W. Zulehner, editors, Scientific
Computing in Electrical Engineering, volume 28 of Mathematics in
Industry, Cham, 2018. Springer.
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DOI ]
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[P235]
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N. Banagaaya, P. Benner, and L. Feng.
Parametric model order reduction for electro-thermal coupled problems
with many inputs.
In Progress in Industrial Mathematics at ECMI 2016, volume 26
of Mathematics in Industry, pages 263--270, 2018.
[ bib |
DOI ]
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[P234]
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X. Du and P. Benner.
Parameterized frequency-dependent balanced truncation for model order
reduction of linear systems.
In 2017 29th Chinese Control And Decision Conference (CCDC),
pages 901--908, May 2017.
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[P233]
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N. Xue and A. Chakrabortty.
LQG control of large networks: A clustering-based approach.
In American Control Conference (ACC), pages 2333--2338, 2017.
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[P232]
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K. Shomalzadeh and T. Amraee.
Unstable power system model reduction using balanced truncation.
In 2017 25th Iranian Conference on Electrical Engineering
(ICEE), page 17045511, 2017.
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[P231]
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S. Kula and A. S. Lup.
Parametrized reduced model of RF MEMS capacitive switch.
In 2017 10th International Symposium on Advanced Topics in
Electrical Engineering (ATEE), pages 529--532, 2017.
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[P230]
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C. Himpe, T. Leibner, and S. Rave.
Comprehensive memory-bound simulations on single board computers.
In Book of Abstracts of 2nd Workshop on Power-Aware Computing,
Ringberg Castle, 2017.
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[P229]
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R. B. Choroszucha and J. Sun.
Empirical Riccati covariance matrices for closed-loop model order
reduction of nonlinear systems by balanced truncation.
In Proceedings of the American Control Conference, pages
3476--3482, 2017.
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[P228]
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P. Benner and S. W. R. Werner.
MORLAB - Modellreduktion in MATLAB.
In T. Meurer and F. Woittennek, editors, Tagungsband GMA-FA 1.30
'Modellierung, Identifikation und Simulation in der Automatisierungstechnik'
und GMA-FA 1.40 'Theoretische Verfahren der Regelungstechnik', Workshop in
Anif, Salzburg, 18.-22.09.2017, pages 508--517, 2017.
[ bib |
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[P227]
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P. Benner, J. Saak, and M. M. Uddin.
Reduced-order modeling of index-1 vibrational systems using
interpolatory projections.
In Proceedings of the 19th International Conference On Computer
and Information Technology, pages 134--138, Dhaka, Bangladesh, Dec. 2016.
IEEE Publications.
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[P226]
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N. Xue and A. Chakrabortty.
H2-clustering of closed-loop consensus networks under
generalized LQR designs.
In IEEE 55th Conference on Decision and Control (CDC), pages
5116--5121, 2016.
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[P225]
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N. Xue and A. Chakrabortty.
H2-clustering of closed-loop consensus networks under a
class of LQR design.
In American Control Conference (ACC), pages 7141--7146, 2016.
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[P224]
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A. Thakallapelli, S. Ghosh, and S. Kamalasadan.
Real-time frequency based reduced order modeling of large power grid.
In 2016 IEEE Power and Energy Society General Meeting (PESGM),
page 16464882, 2016.
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[P223]
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P. Schulze, T. C. Ionescu, and J. M. A. Scherpen.
Families of moment matching-based reduced order models for linear
descriptor systems.
In European Control Conference (ECC), pages 1964--1969, 2016.
[ bib |
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[P222]
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M. Ohlberger, S. Rave, and F. Schindler.
Model reduction for multiscale lithium-ion battery simulation.
In B. Karasözen, M. Manguoğlu, M. Tezer-Sezgin,
S. Göktepe, and ömür Uğur, editors, Numerical
Mathematics and Advanced Applications ENUMATH 2015, volume 112, pages
317--331, 2016.
[ bib |
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[P221]
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P. Mlinarić, S. Grundel, and P. Benner.
Clustering-based model order reduction for multi-agent systems with
general linear time-invariant agents.
In Proceedings of the 22nd International Symposium on
Mathematical Theory of Networks and Systems, pages 230--235, Minneapolis,
MN, USA, 2016.
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[P220]
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S. Lefteriu and S. Grivet-Talocia.
Loewner-based macromodeling with exact interpolation constraints.
In 2016 IEEE 25th Conference on Electrical Performance of
Electronic Packaging And Systems (EPEPS), pages 43--46, 2016.
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[P219]
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X. Lan, H. Zhao, Y. Wang, and Z. Mi.
Nonlinear power system model reduction based on empirical Gramians.
In 2016 IEEE International Conference on Power System Technology
(POWERCON), page 16487940, 2016.
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[P218]
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C. Kweyu, M. Hess, L. Feng, M. Stein, and P. Benner.
Reduced basis method for Poisson-Boltzmann equation.
In ECCOMAS Congress 2016, VII European Congress on Computational
Methods in Applied Sciences and Engineering, volume 2, pages 4187--4195,
2016.
[ bib |
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[P217]
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M. Khatibi and H. Zargarzadeh.
Power system dynamic model reduction by means of an iterative
SVD-Krylov model reduction method.
In 2016 IEEE Power & Energy Society Innovative Smart Grid
Technologies Conference (ISGT), page 16526042, 2016.
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Optimale FE-Reduktion, Moderne Modellreduktion elastischer
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