Machine learning encompasses a broad range of algorithms and modeling tools used for a vast array of data processing tasks, which has entered most scientific disciplines in recent years. We review in a selective way the recent research on the interface between machine learning and physical sciences. This includes conceptual developments in machine learning (ML) motivated by physical insights, applications of machine learning techniques to several domains in physics, and cross-fertilization between the two fields. After giving basic notion of machine learning methods and principles, we describe examples of how statistical physics is used to understand methods in ML. We then move to describe applications of ML methods in particle physics and cosmology, quantum many body physics, quantum computing, and chemical and material physics. We also highlight research and development into novel computing architectures aimed at accelerating ML. In each of the sections we describe recent successes as well as domain-specific methodology and challenges.

中文翻译：

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机器学习与物理科学

机器学习包含用于大量数据处理任务的广泛算法和建模工​​具，近年来已进入大多数科学学科。我们以有选择的方式回顾了有关机器学习与物理科学之间关系的最新研究。这包括以物理见解为动力的机器学习（ML）的概念性发展，机器学习技术在物理领域中的应用以及这两个领域之间的交叉应用。在给出了机器学习方法和原理的基本概念之后，我们描述了如何使用统计物理学来理解ML中的方法的示例。然后，我们开始描述ML方法在粒子物理学和宇宙学，量子多体物理学，量子计算以及化学和材料物理学中的应用。我们还将重点介绍旨在加速ML的新型计算体系结构的研究和开发。在每个部分中，我们都描述了最近的成功以及特定领域的方法和挑战。