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On the Interpolation of Reduced-Order Models

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      Abstract

      A parametric model-order reduction method based on interpolation of reduced-order models, namely the pole-matching method, is proposed for linear systems in the frequency domain. It captures the parametric dynamics of the system by interpolating the positions and amplitudes of the poles. The pole-matching method relies completely on the reduced-order models themselves, regardless of how they are built. It is able to deal with many parameters as well as complicated parameter dependency. Numerical results show that the proposed pole-matching method gives accurate results even when it interpolates two reduced-order models of completely different nature, one computed by a projection-based method and the other computed by a data-driven method.

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      Affiliations
      [1 ]Max Planck Institute for Dynamics of Complex Technical Systems
      [* ]Correspondence: yue@ 123456mpi-magdeburg.mpg.de
      Journal
      ScienceOpen Posters
      ScienceOpen
      27 April 2018
      10.14293/P2199-8442.1.SOP-MATH.NSOQWL.v1
      Copyright © 2018

      This work has been published open access under Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com.

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