Machine learning is a rapidly growing field with the potential to revolutionize many areas of science, including physics. This review provides a brief overview of machine learning in physics, covering the main concepts of supervised, unsupervised, and reinforcement learning, as well as more specialized topics such as causal inference, symbolic regression, and deep learning. We present some of the principal applications of machine learning in physics and discuss the associated challenges and perspectives.

翻译：机器学习是一个快速发展的领域，有潜力彻底改变包括物理学在内的众多科学领域。本综述简要概述了物理学中的机器学习，涵盖了监督学习、无监督学习和强化学习的主要概念，以及因果推断、符号回归和深度学习等更专门的主题。我们介绍了机器学习在物理学中的一些主要应用，并讨论了相关的挑战与前景。