Advanced statistical learning method for multi-physics NDT-NDE

This work presents an innovative multi-physics (MP) Learning-by-Examples (LBE) inversion methodology for real-time non-destructive testing (NDT). Eddy Current Testing (ECT) and Ultrasonic Testing (UT) data are effectively combined to deal with the localization and characterization of a crack inside a conductive structure. An adaptive sampling strategy is applied on ECT-UT data in order to build an optimal (i.e., having minimum cardinality and highly informative) training set. Support vector regression (SVR) is exploited to obtain a computationally-efficient and accurate surrogate model of the inverse operator and, subsequently, to perform real-time inversions on previously-unseen measurements provided by simulations. The robustness of the proposed MP-LBE approach is numerically assessed in presence of synthetic noisy test set and compared to single-physic (i.e., ECT or UT) inversion.