Machine learning transforms the inference of the nuclear equation of state

被引：0

作者：

Wang Yongjia ^{[1
]}

Li Qingfeng ^{[1
,2
,3
]}

机构：

[1] School of Science, Huzhou University, Huzhou , China

[2] Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou , China

[3] School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing ,

来源：

Frontiers of Physics | 2023年 / 18卷 / 06期

关键词：

nuclear equation of state; machine learning; nuclear theory; nuclear experiment;

D O I：

暂无

中图分类号：

TP181 [自动推理、机器学习]; O571 [原子核物理学];

学科分类号：

0827 ; 082701 ;

摘要：

Our knowledge of the properties of dense nuclear matter is usually obtained indirectly via nuclear experiments, astrophysical observations, and nuclear theory calculations. Advancing our understanding of the nuclear equation of state (EOS, which is one of the most important properties and of central interest in nuclear physics) has relied on various data produced from experiments and calculations. We review how machine learning is revolutionizing the way we extract EOS from these data, and summarize the challenges and opportunities that come with the use of machine learning.

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