Metamodel-based Markov-Chain-Monte-Carlo parameter inversion applied in eddy current flaw characterization - CentraleSupélec Accéder directement au contenu
Article Dans Une Revue NDT & E International Année : 2018

Metamodel-based Markov-Chain-Monte-Carlo parameter inversion applied in eddy current flaw characterization

Résumé

Flaw characterization in eddy current testing usually requires to solve a nonlinear inverse problem. Due to high computational cost, Markov Chain Monte Carlo (MCMC) methods are hardly employed since often needing many forward evaluations. However, they have good potential in dealing with complicated forward models and they do not reduce to only providing the parameters sought. Here, we introduce a computationally-cheap surrogate forward model into a MCMC algorithm for eddy current flaw characterization. Due to the use of a database trained off-line, we benefit from the MCMC algorithm for getting more information and we do not suffer from the computational burden. Numerous experiments are carried out to validate the approach. The results include not only the estimated parameters, but also standard deviations, marginal densities and correlation coefficients between two parameters of interest.
Fichier principal
Vignette du fichier
Metamodel_based_MCMC_parameter_inversion_applied_in_ECT-Caietal-Preprint.pdf (9.61 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01634071 , version 1 (03-03-2020)

Identifiants

Citer

Caifang Cai, Roberto Miorelli, Marc Lambert, Thomas Rodet, Dominique Lesselier, et al.. Metamodel-based Markov-Chain-Monte-Carlo parameter inversion applied in eddy current flaw characterization. NDT & E International, 2018, 99, pp.13-22. ⟨10.1016/j.ndteint.2018.02.004⟩. ⟨hal-01634071⟩
557 Consultations
214 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More