Reference : Identifying elastoplastic parameters with Bayes' theorem considering double error sou...
Scientific journals : Article
Engineering, computing & technology : Aerospace & aeronautics engineering
Engineering, computing & technology : Civil engineering
Engineering, computing & technology : Computer science
Engineering, computing & technology : Materials science & engineering
Engineering, computing & technology : Mechanical engineering
Engineering, computing & technology : Multidisciplinary, general & others
Computational Sciences
http://hdl.handle.net/10993/36610
Identifying elastoplastic parameters with Bayes' theorem considering double error sources and model uncertainty
English
Rappel, Hussein mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
Beex, Lars mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
Noels, Ludovic [University of Liege > Aerospace and Mechanical Engineering Department > > Associate Professor]
Bordas, Stéphane mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
Jan-2019
Probabilistic Engineering Mechanics
Elsevier
55
28-41
Yes (verified by ORBilu)
International
0266-8920
1878-4275
Amsterdam
The Netherlands
[en] Bayesian inference ; Bayes' theorem ; stochastic identification ; parameter identification ; elastoplasticity ; plasticity ; model uncertainty
[en] We discuss Bayesian inference for the identi cation of elastoplastic material parameters. In addition to errors in the stress measurements, which are commonly considered, we furthermore consider errors in the strain measurements. Since a difference between the model and the experimental data may still be present if the data is not contaminated by noise, we also incorporate the possible error of the model itself. The three formulations to describe model uncertainty in this contribution are: (1) a random variable which is taken from a normal distribution with constant parameters, (2) a random variable which is taken from a normal distribution with an input-dependent mean, and (3) a Gaussian random process with a stationary covariance function. Our results show that incorporating model uncertainty often, but not always, improves the results. If the error in the strain is considered as well, the results improve even more.
University of Luxembourg: Institute of Computational Engineering
University of Luxembourg - UL
Researchers ; Professionals
http://hdl.handle.net/10993/36610
10.1016/j.probengmech.2018.08.004

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