In simulation-based realization of complex systems, we are forced to address the issue of
computational complexity. One critical issue that must be addressed is the approximation of …

The expected improvement (EI) algorithm is a very popular method for expensive
optimization problems. In the past twenty years, the EI criterion has been extended to deal …

Computer simulation experiments are essential to modern scientific discovery, whether that
be in physics, chemistry, biology, epidemiology, ecology, engineering, etc. Surrogates are …

Abstract Although Bayesian Optimization (BO) has been employed for accelerating materials
design in computational materials engineering, existing works are restricted to problems …

Metamodeling is becoming a rather popular means to approximate the expensive
simulations in today's complex engineering design problems since accurate metamodels …

Practitioners often encounter challenging real-world problems that involve a simultaneous
optimization of multiple objectives in a complex search space. To address these problems …

This article provides an overview of multi-fidelity modeling trends. Fidelity in modeling refers
to the level of detail and accuracy provided by a predictive model or simulation. Generally …

We use a previously unexplored Bayesian optimization framework,“evolutionary Monte
Carlo sampling,” to systematically design the arrangement of defects in an architected …

Kernel methods are among the most popular techniques in machine learning. From a
regularization perspective they play a central role in regularization theory as they provide a …

Experiments have long been used to study the relationship between a set of inputs to a
physical system and the resulting output. Termed physical experiments in this text, there is a …