Reference : A Tutorial on Bayesian Inference to Identify Material Parameters in Solid Mechanics

	Document type :	Scientific journals : Article
	Discipline(s) :	Physical, chemical, mathematical & earth Sciences : Multidisciplinary, general & others Engineering, computing & technology : Civil engineering 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/10993/37698

Title :	A Tutorial on Bayesian Inference to Identify Material Parameters in Solid Mechanics
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 >]
	Hale, Jack [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
	Noels, Ludovic []
	Bordas, Stéphane [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
Publication date :	1-Jan-2019
Journal title :	Archives of Computational Methods in Engineering
Publisher :	International Center for Numerical Methods in Engineering
Pages :	1-25
Peer reviewed :	Yes
Audience :	International
ISSN :	1134-3060
City :	Barcelona
Country :	Spain
Keywords :	[en] Bayesian inference ; Bayes’ theorem ; stochastic identification ; statistical identification ; parameter identification ; elastoplasticity, ; plasticity
Abstract :	[en] The aim of this contribution is to explain in a straightforward manner how Bayesian inference can be used to identify material parameters of material models for solids. Bayesian approaches have already been used for this purpose, but most of the literature is not necessarily easy to understand for those new to the field. The reason for this is that most literature focuses either on complex statistical and machine learning concepts and/or on relatively complex mechanical models. In order to introduce the approach as gently as possible, we only focus on stress–strain measurements coming from uniaxial tensile tests and we only treat elastic and elastoplastic material models. Furthermore, the stress–strain measurements are created artificially in order to allow a one-to-one comparison between the true parameter values and the identified parameter distributions.
Funders :	University of Luxembourg - UL ; European Research Council
Name of the research project :	279578 - REALTCUT - Towards real time multiscale simulation of cutting in non-linear materials with applications
Target :	Researchers ; Professionals ; Students ; General public ; Others
Permalink :	http://hdl.handle.net/10993/37698
DOI :	10.1007/s11831-018-09311-x
FnR project :	FnR ; FNR6693582 > Jack Samuel Hale > > Advanced Computational Methods for the Simulation of Cutting in Surgery > 01/01/2014 > 31/12/2015 > 2013

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