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Scientific Computing in Electrical Engineering pp 223–231Cite as

Model Order Reduction for Stochastic Expansions of Electric Circuits

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Part of the book series: Mathematics in Industry ((TECMI,volume 23))

Abstract

We consider dynamical systems modelling linear electric circuits. Physical parameters are replaced by random variables for an uncertainty quantification. The random process satisfying the dynamical system exhibits an expansion into a series with orthonormal basis polynomials. We apply quadrature formulas to determine an approximation of the unknown coefficient functions. The separate systems for the different nodes of a quadrature rule are reinterpreted as a single large system to enable a potential for model order reduction. For comparison, the stochastic Galerkin method is also investigated for the same problem. We focus on balanced truncation techniques for a reduction of the state space in the large systems. Numerical results are presented using a band pass filter.

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Author information

Authors and Affiliations

  1. Institute for Mathematics and Computer Science, Ernst-Moritz-Arndt-Universität Greifswald, Walther-Rathenau-Str. 47, D-17489, Greifswald, Germany

    Roland Pulch

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  1. Roland Pulch
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Correspondence to Roland Pulch .

Editor information

Editors and Affiliations

  1. Applied Math. & Numerical Analysis, Bergische Universität Wuppertal, Wuppertal, Nordrhein-Westfalen, Germany

    Andreas Bartel

  2. Chair of Electromagnetic Theory, Bergische Universität Wuppertal, Wuppertal, Nordrhein-Westfalen, Germany

    Markus Clemens

  3. Applied Math. & Numerical Analysis, Bergische Universität Wuppertal, Wuppertal, Nordrhein-Westfalen, Germany

    Michael Günther

  4. Applied Math. & Numerical Analysis, Bergische Universität Wuppertal, Wuppertal, Germany

    E. Jan W. ter Maten

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Pulch, R. (2016). Model Order Reduction for Stochastic Expansions of Electric Circuits. In: Bartel, A., Clemens, M., Günther, M., ter Maten, E. (eds) Scientific Computing in Electrical Engineering. Mathematics in Industry(), vol 23. Springer, Cham. https://doi.org/10.1007/978-3-319-30399-4_22

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