Stochastic Metamodel for Probability of Detection Estimation of Eddy-Current Testing Problem in Random Geometric

The calculation of the probability of detection in non-destructive eddy-current testing requires the solution of a stochastic model requiring numerous calls of a numerical model leading to a huge computation time. To reduce this computation time, we propose in this paper to combine either the use of a stochastic metamodel or a mapping which avoids the remeshing step. The stochastic metamodel is constructed using the least-angle regression method. This approach is tested on an axisymmetric problem with six random input paramters which shows its efficiency and its accuracy.