Metamodel-based nested sampling for model selection in eddy-current testing

Abstract : Flaw characterization in eddy current testing usually involves to solve a non-linear inverse problem. Due to high computational cost, sampling algorithms are hardly employed since often requiring to evaluate the forward model many times. However, they have good potential in dealing with complicated forward models. Here, we replace the original forward model by a computationally-cheap surrogate model. Then, we apply a Markov Chain Monte Carlo (MCMC) algorithm to tackle the inversion. The expensive database training part is shifted to off-line calculation. So, we benefit from the MCMC algorithm due to its high estimation accuracy, and do not suffer from the computational burden
Type de document :
Article dans une revue
IEEE Transactions on Magnetics, Institute of Electrical and Electronics Engineers, 2017, 53 (4), pp.6200912. 〈10.1109/TMAG.2016.2635626〉
Liste complète des métadonnées

https://hal-centralesupelec.archives-ouvertes.fr/hal-01397025
Contributeur : Dominique Lesselier <>
Soumis le : mardi 15 novembre 2016 - 13:07:22
Dernière modification le : jeudi 13 septembre 2018 - 15:24:04

Identifiants

Citation

Caifang Cai, Sandor Bilicz, Thomas Rodet, Marc Lambert, Dominique Lesselier. Metamodel-based nested sampling for model selection in eddy-current testing. IEEE Transactions on Magnetics, Institute of Electrical and Electronics Engineers, 2017, 53 (4), pp.6200912. 〈10.1109/TMAG.2016.2635626〉. 〈hal-01397025〉

Partager

Métriques

Consultations de la notice

534