Comptes Rendus
Real-time Bayesian data assimilation with data selection, correction of model bias, and on-the-fly uncertainty propagation
Comptes Rendus. Mécanique, Volume 347 (2019) no. 11, pp. 762-779.

The work introduces new advanced numerical tools for data assimilation in structural mechanics. Considering the general Bayesian inference context, the proposed approach performs real-time and robust sequential updating of selected parameters of a numerical model from noisy measurements, so that accurate predictions on outputs of interest can be made from the numerical simulator. The approach leans on the joint use of Transport Map sampling and PGD model reduction into the Bayesian framework. In addition, a procedure for the dynamical and data-based correction of model bias during the sequential Bayesian inference is set up, and a procedure based on sensitivity analysis is proposed for the selection of the most relevant data among a large set of data, as encountered for instance with full-field measurements coming from digital image/volume correlation (DIC/DVC) technologies. The performance of the overall numerical strategy is illustrated on a specific example addressing structural integrity on damageable concrete structures, and dealing with the prediction of crack propagation from a damage model and DIC experimental data.

Published online:
DOI: 10.1016/j.crme.2019.11.004
Keywords: Data assimilation, Bayesian inference, Model reduction, Modeling error, Real-time simulations, Full-field measurements, Uncertainty quantification

Paul-Baptiste Rubio 1; Ludovic Chamoin 1, 2; François Louf 1

1 LMT (ENS Paris-Saclay, CNRS, Université Paris-Saclay), 61, avenue du Président-Wilson, 94235 Cachan, France
2 Institut Universitaire de France (IUF), 1 rue Descartes, 75005 Paris, France
     author = {Paul-Baptiste Rubio and Ludovic Chamoin and Fran\c{c}ois Louf},
     title = {Real-time {Bayesian} data assimilation with data selection, correction of model bias, and on-the-fly uncertainty propagation},
     journal = {Comptes Rendus. M\'ecanique},
     pages = {762--779},
     publisher = {Elsevier},
     volume = {347},
     number = {11},
     year = {2019},
     doi = {10.1016/j.crme.2019.11.004},
     language = {en},
AU  - Paul-Baptiste Rubio
AU  - Ludovic Chamoin
AU  - François Louf
TI  - Real-time Bayesian data assimilation with data selection, correction of model bias, and on-the-fly uncertainty propagation
JO  - Comptes Rendus. Mécanique
PY  - 2019
SP  - 762
EP  - 779
VL  - 347
IS  - 11
PB  - Elsevier
DO  - 10.1016/j.crme.2019.11.004
LA  - en
ID  - CRMECA_2019__347_11_762_0
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%0 Journal Article
%A Paul-Baptiste Rubio
%A Ludovic Chamoin
%A François Louf
%T Real-time Bayesian data assimilation with data selection, correction of model bias, and on-the-fly uncertainty propagation
%J Comptes Rendus. Mécanique
%D 2019
%P 762-779
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%I Elsevier
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Paul-Baptiste Rubio; Ludovic Chamoin; François Louf. Real-time Bayesian data assimilation with data selection, correction of model bias, and on-the-fly uncertainty propagation. Comptes Rendus. Mécanique, Volume 347 (2019) no. 11, pp. 762-779. doi : 10.1016/j.crme.2019.11.004.

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