The vast quantity of information brought by big data as well as the evolving computer
hardware encourages success stories in the machine learning community. In the …

As a non-parametric Bayesian model which produces informative predictive distribution,
Gaussian process (GP) has been widely used in various fields, like regression, classification …

CoKriging is a popular surrogate modeling approach to approximate the input–output
relationship using multi-fidelity data from different sources. However, it suffers from the big …

Complex constrained global optimization problems such as optimal actuators placement are
extremely challenging. Such challenges, including nonlinearity and nonstationarity of …

In this paper we introduce a novel model for Gaussian process (GP) regression in the fully
Bayesian setting. Motivated by the ideas of sparsification, localization and Bayesian additive …

Gaussian processes (GPs) are a versatile nonparametric model for nonlinear regression
and have been widely used to study spatiotemporal phenomena. However, standard GPs …

High-fidelity physics simulations, such as the Energy Exascale Earth System Model (E3SM),
are generating ever increasing quantities of data. In the near future, it will be infeasible to …

Gaussian process regression and ordinary kriging are effective methods for spatial
estimation, but are generally not used in online trajectory-planning applications for …

Autonomous vehicles for exploration purposes are often limited by energy and computation
capacity. Usually they are tasked with the goal of efficiently and optimally exploring a given …

Linking the highly complex morphology of organic photovoltaic (OPV) thin films to their
charge transport properties is critical for achieving high performance material systems that …