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

Ziming Liu; Yunyue Chen; Yuanqi Du; Max Tegmark

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arXiv · 2109.13901

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

Ziming Liu·Yunyue Chen·Yuanqi Du·Max Tegmark

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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.

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BibTeX RIS

Ziming Liu, Yunyue Chen, Yuanqi Du, Max Tegmark. 2021-09-28. Physics-Augmented Learning: A New Paradigm Beyond Physics-Informed Learning. https://arxiv.org/abs/2109.13901

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