[en] We discuss Bayesian inference (BI) for the probabilistic identification of material parameters. This contribution aims to shed light on the use of BI for the identification of elastoplastic material parameters. For this purpose a single spring is considered, for which the stress-strain curves are artificially created. Besides offering a didactic introduction to BI, this paper proposes an approach to incorporate statistical errors both in the measured stresses, and in the measured strains. It is assumed that the uncertainty is only due to measurement errors and the material is homogeneous. Furthermore, a number of possible misconceptions on BI are highlighted based on the purely elastic case.
Disciplines :
Ingénierie civile Physique, chimie, mathématiques & sciences de la terre: Multidisciplinaire, généralités & autres Science des matériaux & ingénierie Ingénierie mécanique Ingénierie, informatique & technologie: Multidisciplinaire, généralités & autres
Auteur, co-auteur :
RAPPEL, Hussein ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit
BEEX, Lars ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit
HALE, Jack ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit
BORDAS, Stéphane ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit
Langue du document :
Anglais
Titre :
Bayesian inference for the stochastic identification of elastoplastic material parameters: Introduction, misconceptions and insights
FP7 - 279578 - REALTCUT - Towards real time multiscale simulation of cutting in non-linear materials with applications to surgical simulation and computer guided surgery
Organisme subsidiant :
University of Luxembourg - UL CER - Conseil Européen de la Recherche CE - Commission Européenne European Union
