We propose a batch Bayesian active learning technique for feasible region identification by assuming that the constraint function is Lipschitz continuous.

This work proposes a batch Bayesian active learning technique for feasible region identification by assuming that the constraint function is Lipschitz ...

May 13, 2021 · Identifying all designs satisfying a set of constraints is an important part of the engineering design process.

ABSTRACT. Identifying all designs satisfying a set of constraints is an important part of the engineering design process. With physics-based simulation ...

We investigate a simple heuristic based on an estimate of the Lipschitz constant that captures the most important aspect of this interaction (i.e. local ...

2021. Batch bayesian active learning for feasible region identification by local penalization. J Qing, N Knudde, I Couckuyt, T Dhaene, K Shintani. 2020 Winter ...

Here we show how Trieste can be used to identify failure or feasible regions with the help of acquisition functions designed with this goal in mind.

This work proposes a batch Bayesian active learning technique for feasible region identification by assuming that the constraint function is Lipschitz ...

Article "Batch Bayesian Active Learning For Feasible Region Identification by Local Penalization" Detailed information of the J-GLOBAL is an information ...

identify L. In the BO penalization context, small but feasible values of L are preferred, because they produce large exclusion zones and thus more efficient ...