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

Kavli Affiliate: Max Tegmark

| First 5 Authors: Ziming Liu, Yunyue Chen, Yuanqi Du, Max Tegmark,

| Summary:

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.

| Search Query: ArXiv Query: search_query=au:”Max Tegmark”&id_list=&start=0&max_results=10

Read More

Leave a Reply

Your email address will not be published.