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Sensitivity Analysis of Random Linear Dynamical Models Using System Norms

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Scientific Computing in Electrical Engineering (SCEE 2022)

Part of the book series: Mathematics in Industry ((TECMI,volume 43))

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Abstract

We consider linear dynamical systems with a single output, where the systems include random parameters to perform an uncertainty quantification. Using the concept of polynomial chaos, a linear stochastic Galerkin system of higher dimension with multiple outputs is arranged. Quadratic combinations of the outputs yield approximations of time-dependent indices in global sensitivity analysis, which indicate the influence of each random parameter. We investigate system norms for the quadratic outputs, because these norms generate time-independent sensitivity measures. Numerical results are presented for a model of an electric circuit.

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Author information

Authors and Affiliations

  1. Institute of Mathematics and Computer Science, Universität Greifswald, Walther-Rathenau-Str. 47, 17489, Greifswald, Germany

    Roland Pulch

Authors
  1. Roland Pulch
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Correspondence to Roland Pulch .

Editor information

Editors and Affiliations

  1. Eindhoven University of Technology, Eindhoven, The Netherlands

    Martijn van Beurden

  2. DIAM, Numerical Analysis, Delft University of Technology, Delft, Zuid-Holland, The Netherlands

    Neil V. Budko

  3. Politehnica University of Bucharest, Bucharest, Romania

    Gabriela Ciuprina

  4. Department Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands

    Wil Schilders

  5. Eindhoven University of Technology, Eindhoven, The Netherlands

    Harshit Bansal

  6. INESC-ID, Lisbon, Portugal

    Ruxandra Barbulescu

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© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Pulch, R. (2024). Sensitivity Analysis of Random Linear Dynamical Models Using System Norms. In: van Beurden, M., Budko, N.V., Ciuprina, G., Schilders, W., Bansal, H., Barbulescu, R. (eds) Scientific Computing in Electrical Engineering. SCEE 2022. Mathematics in Industry(), vol 43. Springer, Cham. https://doi.org/10.1007/978-3-031-54517-7_24

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