## Interpolatory Projection Methods for Parameterized Model Reduction

Citations: | 3 - 2 self |

### BibTeX

@MISC{Baur_interpolatoryprojection,

author = {Ulrike Baur and Serkan Gugercin and et al.},

title = {Interpolatory Projection Methods for Parameterized Model Reduction},

year = {}

}

### OpenURL

### Abstract

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 H2 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 H2 ⊗ L2 joint error measure. The capabilities of these approaches are illustrated by several numerical