Artificial Intelligence and Machine Learning in Nuclear Physics

• URL: http://arxiv.org/abs/2112.02309v1
• Date: Sat, 4 Dec 2021 11:26:00 GMT
• Title: Artificial Intelligence and Machine Learning in Nuclear Physics
• Authors: Amber Boehnlein, Markus Diefenthaler, Cristiano Fanelli, Morten Hjorth-Jensen, Tanja Horn, Michelle P. Kuchera, Dean Lee, Witold Nazarewicz, Kostas Orginos, Peter Ostroumov, Long-Gang Pang, Alan Poon, Nobuo Sato, Malachi Schram, Alexander Scheinker, Michael S. Smith, Xin-Nian Wang, Veronique Ziegler
• Abstract summary: This Review gives a snapshot of nuclear physics research transformed by artificial intelligence and machine learning techniques. These techniques are being applied across the diversity of nuclear physics research topics, leading to advances that will facilitate scientific discoveries and societal applications.
• Score: 40.019577714454
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Advances in artificial intelligence/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 Review gives a snapshot of nuclear physics research which has been transformed by artificial intelligence and machine learning techniques.

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