Stochastic Galerkin methods and model order reduction for linear dynamical systems

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Samenvatting

Linear dynamical systems are considered in form of ordinary differential equations or differential algebraic equations. We change their physical parameters into random variables to represent uncertainties. A stochastic Galerkin method yields a larger linear dynamic al system satisfied by an approximation of the random processes. If the original systems own a high dimensionality, then a model order reduction is required to decrease the complexity. We investigate two approaches: the system of the stochastic Galerkin scheme is reduced and, vice versa, the original systems are reduced followed by an application of the stochastic Galerkin method. The properties are analyzed in case of reductions based on moment matching with the Arnoldi algorithm. We present numerical computations for two test examples.
Originele taal-2Engels
Plaats van productieEindhoven
UitgeverijTechnische Universiteit Eindhoven
Aantal pagina's27
StatusGepubliceerd - 2013

Publicatie series

NaamCASA-report
Volume1331
ISSN van geprinte versie0926-4507

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  • Citeer dit

    Pulch, R., & Maten, ter, E. J. W. (2013). Stochastic Galerkin methods and model order reduction for linear dynamical systems. (CASA-report; Vol. 1331). Technische Universiteit Eindhoven.