UQSay #71

2–3 PM — Morgane Menz (IFPEN) — [slides]

Archissur: an Active Recovery of Constrained and Hidden Sets by SUR method. Coupling with adaptive space-filling and optimization

The analysis of simulated engineering systems (robust optimization, reliability assessment, …) generally requires numerous computationally expensive code simulations with different possible sets of values of design and environmental input variables. However, the simulators can encounter simulation crashes due to convergence issues for some values of both input variables. These failures correspond to a hidden constraint and might be as costly to evaluate as a feasible simulation. The presence of such crashes must be managed in a wise way, in order to target feasible input areas and thus avoid unnecessary irrelevant simulations.

In this context, we propose an adaptive strategy to learn the hidden constraint at a reduced numerical cost based only on a limited number of binary observations corresponding to failure or non-failure status. Our approach is a Gaussian Process Classifier active learning method based on Stepwise Uncertainty Reduction strategies to assess hidden constraints prediction. A numerically effective formulation of the enrichment criterion suited for classification is provided.
Additionally, the proposed enrichment criterion is employed to address metamodeling and optimization in the presence of hidden constraints..

Reference: Estimation of simulation failure set with active learning based on Gaussian Process classifiers and random set theory, 2023.

Joint work with Miguel Munoz-Zuniga (IFPEN) & Delphine Sinoquet (IFPEN).