Artificial intelligence-based techniques, particularly in machine learning and optimization, are increasingly being used in many areas of experimental and theoretical physics to facilitate discovery, accelerate data analysis and modeling efforts, and bridge different physical and temporal scales in numerical models.

These techniques are proving to be powerful tools for advancing our understanding; however, they are not without significant challenges. The theoretical foundations of many tools, such as deep learning, are poorly understood, resulting in the use of techniques whose behavior (and misbehavior) is difficult to predict and understand. Similarly, physicists typically use general AI techniques that are not tailored to the needs of the experimental and theoretical work being done. Thus, many opportunities exist for major advances both in physical discovery using AI and in the theory of AI. Furthermore, there are tremendous opportunities for these fields to inform each other, for example, in creating machine learning- based methods that must obey certain constraints by design, such as the conservation of mass, momentum and energy.