Colloquium : Machine learning in nuclear physics
| Autor: | Amber Boehnlein, Markus Diefenthaler, Nobuo Sato, Malachi Schram, Veronique Ziegler, Cristiano Fanelli, Morten Hjorth-Jensen, Tanja Horn, Michelle P. Kuchera, Dean Lee, Witold Nazarewicz, Peter Ostroumov, Kostas Orginos, Alan Poon, Xin-Nian Wang, Alexander Scheinker, Michael S. Smith, Long-Gang Pang |
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| Rok vydání: | 2022 |
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| Zdroj: | Reviews of Modern Physics. 94 |
| ISSN: | 1539-0756 0034-6861 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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