Nanoporous materials (NPMs) could be used to store, capture, and sense many different
gases. Given an adsorption task, we often wish to search a library of NPMs for the one with …

We consider the problem of optimizing hybrid structures (mixture of discrete and continuous
input variables) via expensive black-box function evaluations. This problem arises in many …

This paper describes a general-purpose extension of max-value entropy search, a popular
approach for Bayesian Optimisation (BO). A novel approximation is proposed for the …

Bayesian optimization (BO) is an efficient framework for solving black-box optimization
problems with expensive function evaluations. This paper addresses the BO problem setting …

In reinforcement learning, the optimism in the face of uncertainty (OFU) is a mainstream
principle for directing exploration towards less explored areas, characterized by higher …

Real-world problems often involve the optimization of several objectives under multiple
constraints. An example is the hyper-parameter tuning problem of machine learning …

Many problems in engineering design and simulation require balancing competing
objectives under the presence of uncertainty. Sample-efficient multiobjective optimization …

The development of a general‐purpose machine learning algorithm capable of quickly
identifying optimal 3D‐printing settings can save manufacturing time and cost, reduce labor …

With advancements in automation and high-throughput techniques, complex materials
discovery with multiple conflicting objectives can now be tackled in experimental labs. Given …

Expensive constrained multi-objective optimization problems (ECMOPs) present a
significant challenge to surrogate-assisted evolutionary algorithms (SAEAs) in effectively …