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Bethe Forum

"Machine Learning: Where to Apply

in Theoretical Physics"

Preliminary Lecture

 

by Jim Halverson (Northeastern University, Boston)

 

Recent Results at the Intersection of Machine Learning and Theoretical Physics

Abstract: In this talk I will provide a birds-eye-view of some results in machine learning and theoretical physics from the last year, including their motivation and techniques. Topics discussed will include machine learning for Calabi-Yau metrics, knot theory, lattice-QCD, and a correspondence between QFT and neural networks.

 

June 8, 2021 - 3 - 5 pm

Online meeting

Bethe Center for Theoretical Physics, Bonn