Gaussian processes (Kriging) are interpolating data-driven models that are frequently
applied in various disciplines. Often, Gaussian processes are trained on datasets and are …

Bayesian optimization is a principled approach for globally optimizing expensive, black-box
functions by using a surrogate model of the objective. However, each step of Bayesian …

This paper presents a recursive reasoning formalism of Bayesian optimization (BO) to model
the reasoning process in the interactions between boundedly rational, self-interested agents …

Bayesian optimization devolves the global optimization of a costly objective function to the
global optimization of a sequence of acquisition functions. This inner-loop optimization can …

Motivated by the challenges in detecting extremely rare failures for sophisticated
specifications in circuit design, we consider the problem of detecting regions of interest …

Bayesian optimization relies on iteratively constructing and optimizing an acquisition
function. The latter turns out to be a challenging, non-convex optimization problem itself …

Albeit the tremendous performance improvements in designing complex artificial
intelligence (AI) systems in data-intensive domains, the black-box nature of these systems …

Bayesian optimization and Lipschitz optimization have developed alternative techniques for
optimizing black-box functions. They each exploit a different form of prior about the function …

Optimization with Trained Machine Learning Models Embedded Page 1 O Optimization with
Trained Machine Learning Models Embedded Artur M. Schweidtmann1, Dominik Bongartz2 …

A high fidelity fluid-structure interaction simulation may require many days to run, on
hundreds of cores. This poses a serious burden, both in terms of time and economic …