Engineering design optimization often uses computer simulations to evaluate candidate designs. Such numerical simulations may consistently fail for some designs, but the failure reason being unknown. If such failures are frequent than the effectiveness of the optimization process can severely degrade. To address this issue this study describes the integration of classifiers, borrowed from the domain of machine learning, into the optimization search. The classifiers attempt to predict if a candidate design will cause a simulation crash, and this prediction is then used to bias the search. The effectiveness of the approach is demonstrated through several numerical experiments.
['Yoel Tenne']
Classifier-assisted optimization