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 …

Surrogate modeling, also called metamodeling, has evolved and been extensively used
over the past decades. A wide variety of methods and tools have been introduced for …

Bayesian optimization provides sample-efficient global optimization for a broad range of
applications, including automatic machine learning, engineering, physics, and experimental …

Many multivariate functions in engineering models vary primarily along a few directions in
the space of input parameters. When these directions correspond to coordinate directions …

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 …

Bayesian optimization is a sample-efficient black-box optimization procedure that is typically
applied to a small number of independent objectives. However, in practice we often wish to …

We consider optimization of composite objective functions, ie, of the form $ f (x)= g (h (x)) $,
where $ h $ is a black-box derivative-free expensive-to-evaluate function with vector-valued …

We study the use of Gaussian process emulators to approximate the parameter-to-
observation map or the negative log-likelihood in Bayesian inverse problems. We prove …

Computer simulations are used to model of complex physical systems. Often, these models
represent the solutions (or at least approximations) to partial differential equations that are …

Solution of statistical inverse problems via the frequentist or Bayesian approaches described
in earlier chapters can be a computationally intensive endeavor, particularly when faced …