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Colloquium: Machine learning in nuclear physics

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
Science Citation Index Expanded
1

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.

关键词

COVARIATE SHIFT NEURAL-NETWORKS CLASSIFICATION IDENTIFICATION PREDICTION MODEL