Identification of Properties of Stochastic Elastoplastic Systems

Bojana V. Rosić*, Hermann G. Matthies

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

3 Citations (Scopus)

Abstract

This paper presents the parameter identification in a Bayesian setting for the elastoplastic problem, mathematically speaking the variational inequality of a second kind. The inverse problem is formulated in a probabilistic manner in which unknown quantities are embedded in a form of the probability distributions reflecting their uncertainty. With the help of the stochastic functional analysis the update procedure is introduced as a direct, purely algebraic way of computing the posterior, which is comparatively inexpensive to evaluate. Such formulation involves the process of solving the convex minimisation problem in a stochastic setting for which the extension of classical optimization algorithm in predictor-corrector form as the solution procedure is proposed. A validation study of identification procedure is done through a series of virtual experiments taking into account the influence of the measurement error and the order of approximation on the posterior estimate.

Original languageEnglish
Title of host publicationComputational Methods in Stochastic Dynamics
Place of PublicationDordrecht
PublisherSpringer
Pages237-253
Number of pages17
Volume2
ISBN (Electronic)978-94-007-5134-7
ISBN (Print)978-94-007-5133-0
DOIs
Publication statusPublished - 1 Jan 2013
Externally publishedYes

Publication series

NameComputational Methods in Applied Sciences
PublisherSpringer
Volume26
ISSN (Print)1871-3033

Keywords

  • Linear Bayesian update
  • Stochastic convex minimisation
  • Stochastic elastoplasticity
  • Stochastic Galerkin method

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  • Cite this

    Rosić, B. V., & Matthies, H. G. (2013). Identification of Properties of Stochastic Elastoplastic Systems. In Computational Methods in Stochastic Dynamics (Vol. 2, pp. 237-253). (Computational Methods in Applied Sciences; Vol. 26). Dordrecht: Springer. https://doi.org/10.1007/978-94-007-5134-7_14