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Data-Driven Model Order Reduction Using Orthonormal Vector Fitting

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Model Order Reduction: Theory, Research Aspects and Applications

Part of the book series: Mathematics in Industry ((TECMI,volume 13))

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Accurate frequency-domain macromodels are becoming increasingly important for the design, study and optimization of complex physical systems. These macromodels approximate the complex frequency-dependent input-output behaviour of broadband multi-port systems in the frequency domain by rational functions [28]. Unfortunately, due to the complexity of the physical systems under study and the dense discretization required for accurately modelling their behaviour, the rational or state-space macromodels may lead to unmanageable levels of storage and computational requirements. Therefore, Model Order Reduction (MOR) methods can be applied to build a model of reduced size, which captures the dynamics of the larger model as closely as possible.

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Authors and Affiliations

  1. Ghent University, Sint Pietersnieuwstraat 41, 9000, Gent, Belgium

    Dirk Deschrijver & Tom Dhaene

Authors
  1. Dirk Deschrijver
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  2. Tom Dhaene
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Editor information

Editors and Affiliations

  1. Physical Design Methods – Mathematics, NXP Semiconductors Corporate I&T/DTF/Design Methods, High Tech Campus 37, 5656, AE Eindhoven, The Netherlands

    Wilhelmus H. A. Schilders

  2. Physical Design Mothods, NXP Semiconductors Corporate I&T/DTF/Design Methods, High Tech Campus 37, 5656, AE Eindhoven, The Netherlands

    Henk A. van der Vorst

  3. Mathematical Institute, Utrecht University, Budapestlaan 6, 3508, TA Utrecht, The Netherlands

    Joost Rommes

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Deschrijver, D., Dhaene, T. (2008). Data-Driven Model Order Reduction Using Orthonormal Vector Fitting. In: Schilders, W.H.A., van der Vorst, H.A., Rommes, J. (eds) Model Order Reduction: Theory, Research Aspects and Applications. Mathematics in Industry, vol 13. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78841-6_16

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