Reference : Identifying elastoplastic parameters with Bayes' theorem considering double error sou...

	Document type :	Scientific journals : Article
	Discipline(s) :	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
	Focus Areas :	Computational Sciences
	To cite this reference:	http://hdl.handle.net/10993/36610

Title :	Identifying elastoplastic parameters with Bayes' theorem considering double error sources and model uncertainty
Language :	English
Author, co-author :	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 >]
	Noels, Ludovic [University of Liege > Aerospace and Mechanical Engineering Department > > Associate Professor]
	Bordas, Stéphane [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
Publication date :	Jan-2019
Journal title :	Probabilistic Engineering Mechanics
Publisher :	Elsevier
Volume :	55
Pages :	28-41
Peer reviewed :	Yes (verified by ORBilu)
Audience :	International
ISSN :	0266-8920
e-ISSN :	1878-4275
City :	Amsterdam
Country :	The Netherlands
Keywords :	[en] Bayesian inference ; Bayes' theorem ; stochastic identification ; parameter identification ; elastoplasticity ; plasticity ; model uncertainty
Abstract :	[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.
Research centres :	University of Luxembourg: Institute of Computational Engineering
Funders :	University of Luxembourg - UL
Target :	Researchers ; Professionals
Permalink :	http://hdl.handle.net/10993/36610
DOI :	10.1016/j.probengmech.2018.08.004

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