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Physics-Augmented Learning: A New Paradigm Beyond Physics-Informed Learning
Ziming Liu · Yuanqi Du · Yunyue Chen · Max Tegmark
Event URL: https://openreview.net/forum?id=suxElmrPNAY »

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.

Author Information

Ziming Liu (MIT)
Yuanqi Du (George Mason University)
Yunyue Chen (King's College London)
Max Tegmark (MIT)

Max Tegmark is a professor doing physics and AI research at MIT, and advocates for positive use of technology as president of the Future of Life Institute. He is the author of over 250 publications as well as the New York Times bestsellers “Life 3.0: Being Human in the Age of Artificial Intelligence” and "Our Mathematical Universe: My Quest for the Ultimate Nature of Reality". His AI research focuses on intelligible intelligence. His work with the Sloan Digital Sky Survey on galaxy clustering shared the first prize in Science magazine’s “Breakthrough of the Year: 2003.”

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