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
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https://hal-centralesupelec.archives-ouvertes.fr/hal-01397025
Contributor : Dominique Lesselier <>
Submitted on : Tuesday, November 15, 2016 - 1:07:22 PM
Last modification on : Thursday, March 21, 2019 - 2:37:19 PM

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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⟩

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