TY - CHAP
T1 - Identification of Properties of Stochastic Elastoplastic Systems
AU - Rosić, Bojana V.
AU - Matthies, Hermann G.
PY - 2013/1/1
Y1 - 2013/1/1
N2 - 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.
AB - 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.
KW - Linear Bayesian update
KW - Stochastic convex minimisation
KW - Stochastic elastoplasticity
KW - Stochastic Galerkin method
UR - http://www.scopus.com/inward/record.url?scp=84963620256&partnerID=8YFLogxK
U2 - 10.1007/978-94-007-5134-7_14
DO - 10.1007/978-94-007-5134-7_14
M3 - Chapter
AN - SCOPUS:84963620256
SN - 978-94-007-5133-0
VL - 2
T3 - Computational Methods in Applied Sciences
SP - 237
EP - 253
BT - Computational Methods in Stochastic Dynamics
PB - Springer
CY - Dordrecht
ER -