Fast model updating coupling Bayesian inference and PGD model reduction

Abstract : The paper focuses on a coupled Bayesian-Proper Generalized Decomposition (PGD) approach for the real-time identification and updating of numerical models. The purpose is to use the most general case of Bayesian inference theory in order to address inverse problems and to deal with different sources of uncertainties (measurement and model errors, stochastic parameters). In order to do so with a reasonable CPU cost, the idea is to replace the direct model called for Monte-Carlo sampling by a PGD reduced model, and in some cases directly compute the probability density functions from the obtained analytical formulation. This procedure is first applied to a welding control example with the updating of a deterministic parameter. In the second application, the identification of a stochastic parameter is studied through a glued assembly example.
Type de document :
Article dans une revue
Computational Mechanics, Springer Verlag, 2018, 62 (6), pp.1485-1509. 〈10.1007/s00466-018-1575-8〉
Liste complète des métadonnées

Littérature citée [35 références]  Voir  Masquer  Télécharger

https://hal.archives-ouvertes.fr/hal-01774547
Contributeur : Paul-Baptiste Rubio <>
Soumis le : samedi 5 mai 2018 - 17:41:08
Dernière modification le : vendredi 22 mars 2019 - 15:22:22
Document(s) archivé(s) le : mardi 25 septembre 2018 - 13:30:15

Fichier

Rubio2018.compressed (1).pdf
Fichiers éditeurs autorisés sur une archive ouverte

Identifiants

Citation

Paul-Baptiste Rubio, François Louf, Ludovic Chamoin. Fast model updating coupling Bayesian inference and PGD model reduction. Computational Mechanics, Springer Verlag, 2018, 62 (6), pp.1485-1509. 〈10.1007/s00466-018-1575-8〉. 〈hal-01774547〉

Partager

Métriques

Consultations de la notice

140

Téléchargements de fichiers

156