Some Contributions to Computational Physics

Abstract

In this talk, I provide an overview of my research in surrogate model error estimation and code verification. For the first part of this talk, I present an approach to engineer features that, with machine-learning regression, can accurately predict the error incurred by reduced-order models and other approximations to parameterized systems of nonlinear equations. In the second part of this talk, I discuss novel approaches to code verification for hypersonics, ablation, and electromagnetics. These approaches include manufactured solutions, methods for algebraic terms, nonintrusive manufactured solutions, and methods for integral equations with singularities.