The computerized simulations of physical and socio-economic systems have proliferated in
the past decade, at the same time, the capability to develop high-fidelity system predictive …

Gaussian process regression is a popular Bayesian framework for surrogate modeling of
expensive data sources. As part of a broader effort in scientific machine learning, many …

The most vital challenge for a radiologist is locating the brain tumors in the earlier stage. As
the brain tumor grows rapidly, doubling its actual size in about twenty-five days. If not dealt …

In this work, we develop Gaussian process regression (GPR) models of isotropic
hyperelastic material behavior. First, we consider the direct approach of modeling the …

This paper presents an approach for constrained Gaussian Process (GP) regression where
we assume that a set of linear transformations of the process are bounded. It is motivated by …

Despite the ubiquity of the Gaussian process regression model, few theoretical results are
available that account for the fact that parameters of the covariance kernel typically need to …

Abstract In Gaussian Process Regression (GPR), hyperparameters are often estimated by
maximizing the marginal likelihood function. However, this data-dominant hyperparameter …

We explore the abilities of two machine learning approaches for no-arbitrage interpolation of
European vanilla option prices, which jointly yield the corresponding local volatility surface …

This article provides an introduction to the asymptotic analysis of covariance parameter
estimation for Gaussian processes. Maximum likelihood estimation is considered. The aim of …

In this paper, we introduce a 3D finite dimensional Gaussian process (GP) regression
approach for learning arbitrage-free swaption cubes. Based on the possibly noisy …