期刊
REVIEWS OF MODERN PHYSICS
卷 91, 期 4, 页码 -出版社
AMER PHYSICAL SOC
DOI: 10.1103/RevModPhys.91.045002
关键词
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资金
- Moore foundation
- Sloan foundation
- Kavli Institute of Theoretical Physics of UCSB
- Institute for Advanced Study
- National Science Foundation [NSF PHY1748958, ACI-1450310, OAC-1836650, DMR-1420073]
- U.S. Army Research Office [W911NF-13-1-0387]
- ERC under the European Unions Horizon 2020 Research and Innovation Programme [714608-SMiLe]
Machine learning (ML) 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. This article reviews in a selective way the recent research on the interface between machine learning and the physical sciences. This includes conceptual developments in ML motivated by physical insights, applications of machine learning techniques to several domains in physics, and cross fertilization between the two fields. After giving a basic notion of machine learning methods and principles, examples are described of how statistical physics is used to understand methods in ML. This review then describes applications of ML methods in particle physics and cosmology, quantum many-body physics, quantum computing, and chemical and material physics. Research and development into novel computing architectures aimed at accelerating ML are also highlighted. Each of the sections describe recent successes as well as domain-specific methodology and challenges.
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