Colloquium : Machine learning in nuclear physics

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 Colloqu...

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Veröffentlicht in:Reviews of modern physics 2022-09, Vol.94 (3), p.1, Article 031003
Hauptverfasser: 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
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Sprache:eng
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Zusammenfassung: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.
ISSN:0034-6861
1539-0756
DOI:10.1103/RevModPhys.94.031003