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INTERPOLATORY PROJECTION METHODS FOR PARAMETERIZED MODEL REDUCTION
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DOI：10.1137/090776925.png)
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Abstract
En 中文
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.
Keywords:
parameterized model reduction
interpolation
rational Krylov
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