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Difference between revisions of "Emgr"

m (https links)
(emgr v5.6)
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== Synopsis ==
 
== Synopsis ==
   
[https://gramian.de emgr] - '''Em'''pirical '''Gr'''amian Framework (Version '''5.5''').
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[https://gramian.de emgr] - '''Em'''pirical '''Gr'''amian Framework (Version '''5.6''').
 
Empirical gramians can be computed for linear and nonlinear control systems for purposes of model order reduction, uncertainty quantification and system identification.
 
Empirical gramians can be computed for linear and nonlinear control systems for purposes of model order reduction, uncertainty quantification and system identification.
 
Model reduction using empirical gramians can be applied to the state space, to the parameter space or to both through combined reduction.
 
Model reduction using empirical gramians can be applied to the state space, to the parameter space or to both through combined reduction.
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* Empirical [[:Wikipedia:Cross_Gramian|Cross Gramian]] (including an Empirical Non-Symmetric Cross Gramian)
 
* Empirical [[:Wikipedia:Cross_Gramian|Cross Gramian]] (including an Empirical Non-Symmetric Cross Gramian)
 
* Empirical Linear [[:Wikipedia:Cross_Gramian|Cross Gramian]]
 
* Empirical Linear [[:Wikipedia:Cross_Gramian|Cross Gramian]]
* Empirical Sensitivity Gramian
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* Empirical Sensitivity Gramian (parameter controllability)
* Empirical Identifiability Gramian
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* Empirical Identifiability Gramian (parameter observability)
* Empirical Joint Gramian
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* Empirical Joint Gramian (parameter observability)
   
 
[[File:emgr-flyer.pdf|thumb|right|emgr overview]]
 
[[File:emgr-flyer.pdf|thumb|right|emgr overview]]

Revision as of 18:56, 2 January 2019


emgr box

Synopsis

emgr - Empirical Gramian Framework (Version 5.6). Empirical gramians can be computed for linear and nonlinear control systems for purposes of model order reduction, uncertainty quantification and system identification. Model reduction using empirical gramians can be applied to the state space, to the parameter space or to both through combined reduction. The emgr framework is a compact open source toolbox for gramian-based model reduction and compatible with OCTAVE and MATLAB.

Features

Application matrix for the empirical gramian framework

emgr encompasses seven types of gramians:

emgr overview

applicable to:

  • Linear + Nonlinear Control Systems
  • First + Second Order Control Systems
  • Parametrized | Parametric Systems
  • Time Invariant + Varying Systems
  • Discretized PDEs

and with sample code for:

  • Balanced Truncation and balancing related methods
  • Combined State and Parameter Reduction
  • Parameter Identification + Sensitivity Analysis
  • Parameter Reduction + Robust Reduction
  • Optimal Sensor + Actuator Placement
  • Decentralized Control
  • Nonlinearity Quantification

References


Links

  • Oberwolfach References on Mathematical Software: Entry

Contact

Christian Himpe