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    Interpolatory projection methods for parameterized model reduction

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    Main article (1.344Mb)
    Downloads: 1046
    Date
    2011
    Author
    Baur, Ulrike
    Beattie, Christopher A.
    Benner, Peter
    Gugercin, Serkan
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    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 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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    http://hdl.handle.net/10919/48155
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    • Scholarly Works, Department of Mathematics [289]

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