- AutorIn
- Ulrike Baur
- Christopher Beattie
- Peter Benner
- Serkan Gugercin
- Titel
- Interpolatory Projection Methods for Parameterized Model Reduction
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:ch1-201000011
- Schriftenreihe
- Chemnitz Scientific Computing Preprints
- Bandnummer
- 09-08
- ISSN
- 1864-0087
- Abstract (EN)
- We provide a unifying projection-based framework for structure-preserving interpolatory model reduction of parameterized linear dynamical systems, i.e., systems having a structured dependence on parameters that we wish to retain in the reduced-order model. The parameter dependence may be linear or nonlinear and is retained in the reduced-order model. Moreover, we are able to give conditions under which the gradient and Hessian of the system response with respect to the system parameters is matched in the reduced-order model. We provide a systematic approach built on established interpolatory $\mathcal{H}_2$ optimal model reduction methods that will produce parameterized reduced-order models having high fidelity throughout a parameter range of interest. For single input/single output systems with parameters in the input/output maps, we provide reduced-order models that are \emph{optimal} with respect to an $\mathcal{H}_2\otimes\mathcal{L}_2$ joint error measure. The capabilities of these approaches are illustrated by several numerical examples from technical applications.
- Andere Ausgabe
- Link: http://www.tu-chemnitz.de/mathematik/csc/preprints.php
- Freie Schlagwörter
- linear dynamical systems
- parameterized model reduction
- rational Krylov
- Klassifikation (DDC)
- 510
- Normschlagwörter (GND)
- Interpolation
- Ordnungsreduktion
- Publizierende Institution
- Technische Universität Chemnitz, Chemnitz
- URN Qucosa
- urn:nbn:de:bsz:ch1-201000011
- Veröffentlichungsdatum Qucosa
- 05.01.2010
- Dokumenttyp
- Preprint
- Sprache des Dokumentes
- Englisch