For more than two decades, there has been increasing interest in develo** frameworks for
the accelerated discovery and design of novel materials that could enable promising and …

Bayesian Optimization (BO), the application of Bayesian function approximation to finding
optima of expensive functions, has exploded in popularity in recent years. In particular, much …

Experimental solid mechanics is at a pivotal point where machine learning (ML) approaches
are rapidly proliferating into the discovery process due to significant advances in data …

Thirty years, 1993–2023, is a huge time frame in science. We address some major
developments in the field of evolutionary algorithms, with applications in parameter …

Many industrial and scientific applications require optimization of one or more objectives by
tuning dozens or hundreds of input parameters. While Bayesian optimization has been a …

Materials design calls for an optimal exploration and exploitation of the process-structure-
property (PSP) relationships to produce materials with targeted properties. Recently, we …

An improved optimization method is proposed which combines the particle swarm
optimization (PSO) algorithm with the dimension reduction kriging surrogate model (DK) …

Digital health applications using Artificial Intelligence (AI) are a promising opportunity to
address the widening gap between available resources and mental health needs globally …

Bayesian optimization (BO) is popular for a analog circuit sizing problem recently. However,
BO can only work well in small-scale circuit. Scaling BO to common circuit optimization …

Many transportation system problems can be formulated as high-dimensional expensive
multi-objective problems. They are challenging for Gaussian process-based Bayesian …