Bayesian optimization (BO) is an efiective surrogate-based method that has been widely
used to optimize simulation-based applications. While the traditional Bayesian optimization …

Design has become a salient part of most of the scientific and engineering tasks, embracing
a wide range of domains including real experimental settings (eg, material discovery or drug …

Abstract Design decisions for complex systems often can be made or informed by a variety
of information sources. When optimizing such a system, the evaluation of a quantity of …

Control of gene regulatory networks (GRNs) to shift gene expression from undesirable states
to desirable ones has received much attention in recent years. Most of the existing methods …

Most multi-fidelity schemes for optimization or reliability assessment rely on regression
surrogates, such as Gaussian processes. Contrary to these approaches, we propose a …

Available computational models for many engineering design applications are both
expensive and and of a black-box nature. This renders traditional optimization techniques …

Multiobjective optimization is often a difficult task owing to the need to balance competing
objectives. A typical approach to handling this is to estimate a Pareto frontier in objective …

Stabilization of complex cyber-physical systems is extremely important in keeping the critical
infrastructure and the environment safe. This is, in particular, critical in coupled …

Engineering systems are often composed of many subsystems that interact with each other.
These subsystems, referred to as disciplines, contain many types of uncertainty and in many …

Multi-objective optimization is often a difficult task owing to the need to balance competing
objectives. A typical approach to handling this is to estimate a Pareto frontier in objective …