Machine learning transforms the inference of the nuclear equation of state

Abstract

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.

Graphical abstract

Keywords

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

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Conflicts of interest

There are no conflicts to declare.

Acknowledgements

The work was supported in part by the National Natural Science Foundation of China (Grant Nos. U2032145 and 11875125) and the National Key Research and Development Program of China (Grant No. 2020YFE0202002).

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