INT 22-1 Highlights Report
Machine Learning for Nuclear Theory
March 28 - April 22, 2022
G. Hagen, N. Sato, P. Shanahan
This program brought together researchers driving the development of machine learning and artificial intelligence for problems in nuclear theory and related areas. Across the month-long program, the workshop hosted a total of 30 talks, including 15 in the intensive embedded workshop week. Physics applications discussed ranged from lattice QCD and statistical systems, hadron and nuclear structure, many-body theory, quantum computing, nuclear astrophysics, and hot and dense matter, while daily discussion sessions brought the participants each week together for topical discussion on key workshop themes, in particular Generative models, Machine learning for state representation, Bayesian inference, Classification, and Explainable AI.