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Machine Learning in Nuclear Physics

Authors :
Boehnlein, Amber
Diefenthaler, Markus
Fanelli, Cristiano
Hjorth-Jensen, Morten
Horn, Tanja
Kuchera, Michelle P.
Lee, Dean
Nazarewicz, Witold
Orginos, Kostas
Ostroumov, Peter
Pang, Long-Gang
Poon, Alan
Sato, Nobuo
Schram, Malachi
Scheinker, Alexander
Smith, Michael S.
Wang, Xin-Nian
Ziegler, Veronique
Publication Year :
2021

Abstract

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 Review gives a snapshot of nuclear physics research which has been transformed by machine learning techniques.<br />Comment: Comments are welcome

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2112.02309
Document Type :
Working Paper
Full Text :
https://doi.org/10.1103/RevModPhys.94.031003