Physics-Augmented Learning: A New Paradigm Beyond Physics-Informed Learning
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.
Cite
Text
Liu et al. "Physics-Augmented Learning: A New Paradigm Beyond Physics-Informed Learning." NeurIPS 2021 Workshops: AI4Science, 2021.Markdown
[Liu et al. "Physics-Augmented Learning: A New Paradigm Beyond Physics-Informed Learning." NeurIPS 2021 Workshops: AI4Science, 2021.](https://mlanthology.org/neuripsw/2021/liu2021neuripsw-physicsaugmented/)BibTeX
@inproceedings{liu2021neuripsw-physicsaugmented,
title = {{Physics-Augmented Learning: A New Paradigm Beyond Physics-Informed Learning}},
author = {Liu, Ziming and Du, Yuanqi and Chen, Yunyue and Tegmark, Max},
booktitle = {NeurIPS 2021 Workshops: AI4Science},
year = {2021},
url = {https://mlanthology.org/neuripsw/2021/liu2021neuripsw-physicsaugmented/}
}