“…In particular, it is important that the meta-model is capable of discriminating the CR from the normal conditions; thus, instead of using an a-priori fixed DOE, a sequential one (where the experimental observations are iteratively and adaptively added to increase the accuracy of the meta-model around the regions of interest) is preferable. The Adaptive Kriging Monte Carlo Simulation (AK-MCS) (Echard et al, 2011) is here employed to this aim. In the AK-MCS, an initial Kriging model is trained with a small set of I/O observations, e.g., sampled according to LHS scheme; then, the algorithm proceeds iteratively according to the following steps: i) randomly sample a large set of input configurations • = c , … , k 8‚ƒ g, e.g., by means of LHS; ii) evaluate the associated responses using the Kriging metamodel " z = cj … , … , j … k 8‚ƒ g; iii) check if a convergence criterion has been reached: if so, the meta-model is sufficiently accurate; otherwise, iv) select, according to a predefined learning function/criterion, the best candidate subset • * ⊂ • to add to the current DOE and evaluate the corresponding real model output " * ; v) retrain a new Kriging meta-model by adding the • * , " * to the training set and go back to step i).…”