Journal
SIAM JOURNAL ON SCIENTIFIC COMPUTING
Volume 33, Issue 5, Pages 2489-2518Publisher
SIAM PUBLICATIONS
DOI: 10.1137/090776925
Keywords
parameterized model reduction; interpolation; rational Krylov
Categories
Funding
- DFG [BE 2174/7-1]
- Automatic, Parameter-Preserving Model Reduction for Applications in Microsystems Technology
- NSF [DMS-0505971, DMS-0645347]
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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 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 optimal with respect to an H-2 circle times L-2 joint error measure. The capabilities of these approaches are illustrated by several numerical examples from technical applications.
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