Deep Learning with Physics Priors as Generalized Regularizers

Published: 28 Oct 2023, Last Modified: 10 Dec 2023NeurIPS2023-AI4Science PosterEveryoneRevisionsBibTeX
Keywords: Structure Risk Minimization, Regularizers, Uncertainties
TL;DR: Leverage knowledge of a physical system as generalized regularizers to minimize structural risk
Abstract: In various scientific and engineering applications, there is typically an approximate model of the underlying complex system, even though it contains both aleatoric and epistemic uncertainties. In this paper, we present a principled method to incorporate these approximate models as physics priors in modeling, to prevent overfitting and enhancing the generalization capabilities of the trained models. Utilizing the structural risk minimization (SRM) inductive principle pioneered by Vapnik, this approach structures the physics priors into generalized regularizers. The experimental results demonstrate that our method achieves up to two orders of magnitude of improvement in testing accuracy.
Submission Track: Original Research
Submission Number: 167