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• W2972903668 abstract "Abstract Mathematical modeling often yields linear dynamical systems in science and engineering. We change physical parameters of the system into random variables to perform an uncertainty quantification. The stochastic Galerkin method yields a larger linear dynamical system, whose solution represents an approximation of random processes. A model order reduction (MOR) of the Galerkin system is advantageous due to the high dimensionality. However, asymptotic stability may be lost in some MOR techniques. In Galerkin-type MOR methods, the stability can be guaranteed by a transformation to a dissipative form. Either the original dynamical system or the stochastic Galerkin system can be transformed. We investigate the two variants of this stability-preserving approach. Both techniques are feasible, while featuring different properties in numerical methods. Results of numerical computations are demonstrated for two test examples modeling a mechanical application and an electric circuit, respectively." @default.
• W2972903668 created "2019-09-19" @default.
• W2972903668 creator A5022413371 @default.
• W2972903668 date "2019-09-10" @default.
• W2972903668 modified "2023-09-25" @default.
• W2972903668 title "Stability-preserving model order reduction for linear stochastic Galerkin systems" @default.
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• W2972903668 doi "https://doi.org/10.1186/s13362-019-0067-6" @default.
• W2972903668 hasPublicationYear "2019" @default.
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