AI for Physics

AI has had a significant impact on physics research in recent years. One area where AI is making a difference is in the analysis of complex data from experiments, such as those conducted at particle accelerators or telescopes. Machine learning techniques are being used to help physicists sift through massive amounts of data to identify patterns and anomalies that may lead to new discoveries. AI is also being applied to problems in theoretical physics, such as optimizing quantum algorithms or predicting the behavior of complex systems. Additionally, AI is helping physicists simulate and model physical processes more efficiently, leading to advances in areas like materials science and fluid dynamics. As representative examples, this chapter provides brief introductions to selected advancements in AI for physics, including unfolding observables from the Large Hadron Collider, magnetic control of tokamak plasmas, and finding evidence for intrinsic charm quarks, based on AI methods such as neural networks, genetic algorithms, reinforcement learning, and more.