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Comparison of methods for parametric model order reduction of instationary problems

Ulrike Baur, Peter Benner, Bernard Haasdonk, Christian Himpe, Immanuel Martini, Mario Ohlberger
Rapport de rechercheMax Planck Institute Magdeburg, Preprint at - february 2015
Dynamical systems of large order appear in many applications. For an efficient simulation it can become necessary to reduce the system dimension using a reliable model order reduction method, in particular in a many-query context when the system is to be solved for varying parameters and input signals. Nowadays, it is often required that the models include physical parameters to allow more exibility in simulation. These parameters should be preserved in the reduced-order system; a task that motivates the development of new approaches to model order reduction referred to collectively as parametric model order reduction. In this work, we compare several methods for parametric model order reduction using common benchmark problems from the literature

Références BibTex

  author       = {Baur, U. and Benner, P. and Haasdonk, B. and Himpe, C. and Martini, I. and Ohlberger, M.},
  title        = {Comparison of methods for parametric model order reduction of instationary problems},
  institution  = {Max Planck Institute Magdeburg},
  month        = {february},
  year         = {2015},
  type         = {Preprint at},
  url          = \{/2015/BBHHMO15},

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