Machine learning has had a substantial impact on physics in recent years, where problems spanning all scales of the universe, from the identification of exoplanets to the detection of anomalies in particle collisions and the prediction of extreme weather events, have seen critical improvements due to applications of deep learning methods. These advances have been made possible through the permeation of recent machine learning advances in the physics community, creating unique insights through the combination of machine learning expertise and physics domain knowledge.
The AI & Physics track will aim to bring together experts working at the interface between machine learning and physics, as well as leading researchers from both the machine learning and physics communities. Machine learning has seen a surge of interest in physics, with techniques ranging from computer vision, natural language processing, generative modelling, and reinforcement learning leading to new methods to solve long-standing problems in the classification, simulation and analysis of physical systems. Areas of particular interest for this track include topics of relevance for the Large Hadron Collider at CERN, applications of machine learning in lattice QCD and cosmology, as well as studies of deep learning models through tools originating from statistical and mathematical physics.
The purpose of this track is to highlight important recent breakthroughs in applications of machine learning in physics, and provide an opportunity to discuss promising new directions in this area, through a combination of diverse expert presentations and a panel discussion.