Bayesian optimization has emerged at the forefront of expensive black-box optimization due
to its data efficiency. Recent years have witnessed a proliferation of studies on the …

Efficiently embedding and/or integrating mechanistic information with data-driven models is
essential if it is desired to simultaneously take advantage of both engineering principles 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 …

Gaussian Processes (GP) can be used as flexible, non-parametric function priors. Inspired
by the growing body of work on Normalizing Flows, we enlarge this class of priors through a …

Additive manufacturing (AM) can be advanced by the diverse characteristics offered by
thermoplastic and thermoset polymers and the further benefits of copolymerization …

A primary goal of computer experiments is to reconstruct the function given by the computer
code via scattered evaluations. Traditional isotropic Gaussian process models suffer from …

Discrete data are abundant and often arise as counts or rounded data. These data
commonly exhibit complex distributional features such as zero-inflation, over-/under …

In the continual effort to improve product quality and decrease operations costs,
computational modeling is increasingly being deployed to determine feasibility of product …

An explosion in the availability of rich data from the technological advances is hindering
efforts at statistical analysis due to constraints on time and memory storage, regardless of …

Gaussian processes are a widely embraced technique for regression and classification due
to their good prediction accuracy, analytical tractability and built-in capabilities for …