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作 者:马余刚 庞龙刚 王睿 周凯 Yu-Gang Ma;Long-Gang Pang;Rui Wang;Kai Zhou(Key Laboratory of Nuclear Physics and Ion-beam Application(MOE),and Institute of Modern Physics,Fudan University,Shanghai 200433,China;Shanghai Research Center for Theoretical Nuclear Physics,NSFC and Fudan University,Shanghai 200438,China;Institute of Particle Physics and Key Laboratory of Quark and Lepton Physics(MOE),Central China Normal University,Wuhan 430079,China;Shanghai Institute of Applied Physics,Chinese Academy of Sciences,Shanghai 201800,China;INFN Sezione di Catania,95123 Catania,Italy;Frankfurt Institute for Advanced Studies(FIAS),D-60438 Frankfurt am Main,Germany)
机构地区:[1]Key Laboratory of Nuclear Physics and Ion-beam Application(MOE),and Institute of Modern Physics,Fudan University,Shanghai 200433,China [2]Shanghai Research Center for Theoretical Nuclear Physics,NSFC and Fudan University,Shanghai 200438,China [3]Institute of Particle Physics and Key Laboratory of Quark and Lepton Physics(MOE),Central China Normal University,Wuhan 430079,China [4]Shanghai Institute of Applied Physics,Chinese Academy of Sciences,Shanghai 201800,China [5]INFN Sezione di Catania,95123 Catania,Italy [6]Frankfurt Institute for Advanced Studies(FIAS),D-60438 Frankfurt am Main,Germany
出 处:《Chinese Physics Letters》2023年第12期34-39,共6页中国物理快报(英文版)
基 金:partially supported by the National Natural Science Foundation of China(Grant Nos. 11890710, 11890714, and 12147101);the BMBF funded KISS consortium (Grant No. 05D23RI1) in the ErUM-Data action plan。
摘 要:In recent years, machine learning(ML) techniques have emerged as powerful tools for studying many-body complex systems, and encompassing phase transitions in various domains of physics. This mini review provides a concise yet comprehensive examination of the advancements achieved in applying ML to investigate phase transitions, with a primary focus on those involved in nuclear matter studies.
关 键 词:TRANSITIONS TRANSITION APPLYING
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