The exchange of ideas between the fields of Artificial Intelligence (AI) and Physics share a long and rich history that led to major developments in both sides. The recent breakthroughs in AI and the rapid growth of Machine Learning (ML) based technology in our day-a-day life has fostered a fresh wave of interest in the synergies between these fields. On one hand, the technological progress in AI has allowed for the fruitful application of Machine Learning techniques in theoretical modelling and experimental data analysis. Indeed, from Molecular Modelling to String Theory, it is hard to name one subfield of Physics which has not been impacted by Machine Learning in the past decade. On the other hand, the lack of a unified theoretical framework allowing to make sense of the challenges raised by the ever-growing practice of Machine Learning has drawn the interest of theoretical Physicists to this field. From the historical connections with Statistical Physics to new inputs coming from High-Energy Physics, ideas and tools from Physics had a major impact in the recent progress of Machine Learning theory.

This track will investigate applications of ML to Physics, ranging from particle physics to biophysics and astronomy, and advancements in understanding ML using tools from Statistical Physics.