Poster
in
Workshop: AI for Science: Mind the Gaps

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

Ziming Liu · Yuanqi Du · Yunyue Chen · Max Tegmark


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.