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- W4381738985 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." @default.
- W4381738985 created "2023-06-24" @default.
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- W4381738985 date "2023-06-22" @default.
- W4381738985 modified "2023-10-11" @default.
- W4381738985 title "Machine learning transforms the inference of the nuclear equation of state" @default.
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- W4381738985 doi "https://doi.org/10.1007/s11467-023-1313-3" @default.
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