Article (Scientific journals)
Identifying elastoplastic parameters with Bayes’ theorem considering output error, input error and model uncertainty
Rappel, Hussein; Beex, Lars A A; Noels, Ludovic et al.
2019In Probabilistic Engineering Mechanics, 55, p. 28-41
Peer Reviewed verified by ORBi
 

Files


Full Text
2018_PEM_BayesianEP.pdf
Author postprint (2.12 MB)
Download

NOTICE: this is the author’s version of a work that was accepted for publication in Computer Methods in Applied Mechanics and Engineering. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Probabilistic Engineering Mechanics # (201#) #–#, DOI: 10.1016/j.probengmech.2018.08.004


All documents in ORBi are protected by a user license.

Send to



Details



Keywords :
Bayesian inferencey; Bayes' theoremt; Stochastic identification; Parameter identification; Elastoplasticity; Plasticity; Model uncertainty
Abstract :
[en] We discuss Bayesian inference for the identification 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 Center/Unit :
A&M - Aérospatiale et Mécanique - ULiège
Disciplines :
Mechanical engineering
Author, co-author :
Rappel, Hussein ;  Université de Liège - ULiège > Form. doct. sc. ingé. & techno. (aéro. & mécan. - Paysage)
Beex, Lars A A;  Université du Luxembourg - UniLu
Noels, Ludovic  ;  Université de Liège - ULiège > Département d'aérospatiale et mécanique > Computational & Multiscale Mechanics of Materials (CM3)
Bordas, Stéphane;  Université du Luxembourg - UniLu
Language :
English
Title :
Identifying elastoplastic parameters with Bayes’ theorem considering output error, input error and model uncertainty
Publication date :
January 2019
Journal title :
Probabilistic Engineering Mechanics
ISSN :
0266-8920
eISSN :
1878-4275
Publisher :
Elsevier, Netherlands
Volume :
55
Pages :
28-41
Peer reviewed :
Peer Reviewed verified by ORBi
European Projects :
FP7 - 279578 - REALTCUT - Towards real time multiscale simulation of cutting in non-linear materials with applications to surgical simulation and computer guided surgery
Name of the research project :
The authors would like to acknowledge the financial support from the University of Luxembourg and the European Research Council Starting Independent Research Grant (ERC Stg grant agreement No. 279578) entitled \Towards real time multiscale simulation of cutting in nonlinear materials with applications to surgical simulation and computer guided surgery.
Funders :
UE - Union Européenne
CE - Commission Européenne
Available on ORBi :
since 05 September 2018

Statistics


Number of views
118 (27 by ULiège)
Number of downloads
141 (9 by ULiège)

Scopus citations®
 
68
Scopus citations®
without self-citations
55
OpenCitations
 
54
OpenAlex citations
 
76

Bibliography


Similar publications



Contact ORBi