Deep Learning with Physics Priors as Generalized Regularizers
Frank Liu Agniva Chowdhury
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.