Colloquium: Machine learning in nuclear physics

作者:Boehnlein, Amber*; Diefenthaler, Markus; Sato, Nobuo; Schram, Malachi; Ziegler, Veronique; Fanelli, Cristiano; Hjorth-Jensen, Morten; Horn, Tanja; Kuchera, Michelle P.; Lee, Dean; Nazarewicz, Witold; Ostroumov, Peter; Orginos, Kostas; Poon, Alan; Wang, Xin-Nian; Scheinker, Alexander; Smith, Michael S.; Pang, Long-Gang
来源:REVIEWS OF MODERN PHYSICS, 2022, 94(3): 031003.
DOI:10.1103/RevModPhys.94.031003

摘要

Advances in machine learning methods provide tools that have broad applicability in scientific research. These techniques are being applied across the diversity of nuclear physics research topics, leading to advances that will facilitate scientific discoveries and societal applications. This Colloquium provides a snapshot of nuclear physics research, which has been transformed by machine learning techniques.

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