| Line 24: | Line 24: | ||
* First + Second Order Systems |
* First + Second Order Systems |
||
* Parametric Systems |
* Parametric Systems |
||
| − | * Time-Invariant Systems |
||
and with sample implementations for: |
and with sample implementations for: |
||
| − | * Balanced Truncation |
+ | * Balanced Truncation + Direct Trunction |
| ⚫ | |||
| − | * Direct Truncation |
||
| ⚫ | |||
* Parameter Reduction |
* Parameter Reduction |
||
| − | * Combined Reduction |
+ | * Combined State and Parameter Reduction |
| − | * Inverse |
+ | * (Bayesian) Inverse Problem Reduction |
== References == |
== References == |
||
| Line 40: | Line 38: | ||
== Links == |
== Links == |
||
| + | |||
* http://gramian.de |
* http://gramian.de |
||
Revision as of 11:49, 5 April 2013
emgr - Empirical Gramian Framework.
Empirical gramians can be computed for linear and nonlinear control systems for purposes of model order reduction or 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
emgr encompasses six types of gramians:
- Empirical Controllability Gramian
- Empirical Observability Gramian
- Empirical Cross Gramian
- Empirical Sensitivity Gramian
- Empirical Identifiability Gramian
- Empirical Joint Gramian
applicable to:
- Linear + Nonlinear Systems
- First + Second Order Systems
- Parametric Systems
and with sample implementations for:
- Balanced Truncation + Direct Trunction
- Parameter Identification + Sensitivity Analysis
- Parameter Reduction
- Combined State and Parameter Reduction
- (Bayesian) Inverse Problem Reduction
References
- C. Himpe, M. Ohlberger "A Unified Software Framework for Empirical Gramians", 2012