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