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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.
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https://hal.archives-ouvertes.fr/hal-01774547
Contributor : Paul-Baptiste Rubio <>
Submitted on : Saturday, May 5, 2018 - 5:41:08 PM
Last modification on : Tuesday, May 14, 2019 - 10:10:52 AM
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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⟩

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