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

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