Physics Is Different: Context, Culture & Craft in Effective AI for Physics

Abstract Physics is a sprawling and ambitious enterprise unified by the goal of explaining the natural world, yet it is extraordinarily diverse in how that goal is pursued. The opportunities to harness artificial intelligence to aid in our pursuits to understand the universe are vast. But the heterogeneity of physics in practice is a vivid demonstration of how scientific inquiry varies across scale, infrastructure, and standards of evidence, and how machine learning integrates unevenly as a result. While current applications of AI often focus on accelerating existing workflows, the deeper promise lies in developing robust methods that plug into physicists’ established workflows and toolkits; lower barriers in the continuous cycle between measurement, simulation, analysis, and theory; and create fundamentally new ways of probing, modeling, and controlling physical systems.