Work

Physics-Augmented Learning: A New Paradigm Beyond Physics-Informed Learning

Year: 2021

Type: preprint

Abstract: Integrating physical inductive biases into machine learning can improve model generalizability. We generalize the successful paradigm of physics-informed learning (PIL) into a more general framework that also includes what we term physics-augmented learning (PAL). PIL and PAL complement each other by handling discriminative and generative properties, respectively. In numerical experiments, we show that PAL performs well on examples where PIL is inapplicable or inefficient.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2109.13901

Authors: Liu, Ziming, Chen, Yunyue, Du, Yuanqi, Tegmark, Max

Institutions: Massachusetts Institute of Technology, King's College London, George Mason University

Language: English

Topic: Model Reduction and Neural Networks

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W3202298006

Cites: 16

Cited by: 14

Related to: 20