Open AccessInforming nuclear physics via machine learning methods with differential and integral experiments
Author(s) -
Denise Neudecker,
Ó. Cabellos,
Alexander Clark,
Michael Grosskopf,
Wim Haeck,
Michal W. Herman,
Jesson Hutchinson,
Toshihiko Kawano,
A. E. Lovell,
Ionel Stetcu,
P. Talou,
Scott Vander Wiel
Publication year - 2021
Publication title -
physical review. c
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.679
H-Index - 235
eISSN - 2469-9993
pISSN - 2469-9985
DOI - 10.1103/physrevc.104.034611
Subject(s) - observable , physics , differential (mechanical device) , neutron , leverage (statistics) , context (archaeology) , statistical physics , nuclear physics , computer science , artificial intelligence , quantum mechanics , paleontology , biology , thermodynamics
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