Stochastic galerkin method for the elastoplasticity problem with uncertain parameters

Bojana V. Rosic; Hermann G. Matthies

doi:10.1007/978-3-642-17484-1_34

Stochastic galerkin method for the elastoplasticity problem with uncertain parameters

Bojana V. Rosic, Hermann G. Matthies

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review

5 Citations (Scopus)

Abstract

The mathematical formulation and numerical simulation of an elastic-plastic material with uncertain parameters in the small strain case is considered. Traditional computational approaches to this problem usually use some form of perturbation or Monte Carlo technique. This is contrasted here with more recent methods based on stochastic Galerkin approximations. In addition, we introduce the characterisation of the variational structure behind the discrete equations defining the closest-point projection approximation in stochastic elastoplasticity.

Original language	English
Title of host publication	Recent Developments and Innovative Applications in Computational Mechanics
Editors	Dana Mueller-Hoeppe, Stefan Loehnert, Stefanie Reese
Place of Publication	Berlin, Heidelberg
Publisher	Springer
Pages	303-310
Number of pages	8
ISBN (Electronic)	978-3-642-17484-1
ISBN (Print)	978-3-642-17483-4
DOIs	https://doi.org/10.1007/978-3-642-17484-1_34
Publication status	Published - 1 Dec 2011
Externally published	Yes

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10.1007/978-3-642-17484-1_34

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Stochastic galerkin method for the elastoplasticity problem with uncertain parameters. / Rosic, Bojana V.; Matthies, Hermann G.

Recent Developments and Innovative Applications in Computational Mechanics. ed. / Dana Mueller-Hoeppe; Stefan Loehnert; Stefanie Reese. Berlin, Heidelberg : Springer, 2011. p. 303-310.

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review

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T1 - Stochastic galerkin method for the elastoplasticity problem with uncertain parameters

AU - Rosic, Bojana V.

AU - Matthies, Hermann G.

PY - 2011/12/1

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N2 - The mathematical formulation and numerical simulation of an elastic-plastic material with uncertain parameters in the small strain case is considered. Traditional computational approaches to this problem usually use some form of perturbation or Monte Carlo technique. This is contrasted here with more recent methods based on stochastic Galerkin approximations. In addition, we introduce the characterisation of the variational structure behind the discrete equations defining the closest-point projection approximation in stochastic elastoplasticity.

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