Aerodynamic modeling plays an important role in multiphysics and design problems, in
addition to experiment and numerical simulation, due to its low-dimensional representation …

Propelled partly by the Materials Genome Initiative, and partly by the algorithmic
developments and the resounding successes of data-driven efforts in other domains …

In this paper, we make the key delineation on the roles of resolution and statistical
uncertainty in hierarchical bandits-based black-box optimization algorithms, guiding a more …

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 …

In many situations across computational science and engineering, multiple computational
models are available that describe a system of interest. These different models have varying …

For many systems in science and engineering, the governing differential equation is either
not known or known in an approximate sense. Analyses and design of such systems are …

Multi-fidelity models provide a framework for integrating computational models of varying
complexity, allowing for accurate predictions while optimizing computational resources …

Multi-output regression problems have extensively arisen in modern engineering
community. This article investigates the state-of-the-art multi-output Gaussian processes …

We propose a new class of Bayesian neural networks (BNNs) that can be trained using
noisy data of variable fidelity, and we apply them to learn function approximations as well as …

Multifidelity surrogates are essential in cases where it is not affordable to have more than a
few high-fidelity samples, but it is affordable to have as many low-fidelity samples as …