Numerically solving partial differential equations (PDEs) can be challenging and
computationally expensive. This has led to the development of reduced-order models …

We introduce a novel method for discerning optical telescope images of stars from those of
galaxies using Gaussian processes (GPs). Although applications of GPs often struggle in …

Gaussian process (GP) regression is a flexible modeling technique used to predict outputs
and to capture uncertainty in the predictions. However, the GP regression process becomes …

Exploration is a challenging problem in reinforcement learning (RL), especially in
environments with sparse rewards. Quantifying and utilizing the parametric uncertainty has …

Gaussian processes (GPs) are Bayesian non-parametric models popular in a variety of
applications due to their accuracy and native uncertainty quantification (UQ). Tuning GP …

Stellar blends, where two or more stars appear blended in an image, pose a significant
visualization challenge in astronomy. Traditionally, distinguishing these blends from single …

Gaussian process (GP) models are effective non-linear models for numerous scientific
applications. However, computation of their hyperparameters can be difficult when there is a …

Kernels representing limiting cases of neural network architectures have recently gained
popularity. However, the application and performance of these new kernels compared to …