ML for Physics and Physics for ML

Shirley Ho · Miles Cranmer

Moderators: Max Welling · Rose Yu

[ Abstract ] [ Slides
Mon 6 Dec 9 a.m. PST — 1 p.m. PST


Physics research and deep learning have a symbiotic relationship, and this bond has become stronger over the past several years. In this tutorial, we will present both sides of this story. How has deep learning benefited from concepts in physics and other sciences? How have different subfields of physics research capitalized on deep learning? What are some yet-unexplored applications of deep learning to physics which could strongly benefit from machine learning? We will discuss the past and present of this intersection, and then theorize possible directions for the future of this connection. In the second part of this talk, we will outline some existing deep learning techniques which have exploited ideas from physics, and point out some intriguing new directions in this area.

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