Neural networks represent densities in a variational extended Thomas-Fermi model, yielding binding energies within 0.5% of prior ETF results and reproducing nuclear pasta phases.
year 2020
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Systematic numerical benchmarking shows Floquet master equation accuracy tracks their underlying assumptions, with secular-approximation versions failing near resonances while non-secular versions show smoother error dependence on drive parameters.
Review summarizing the role of dense-matter equation of state, weak interactions, and r-process nucleosynthesis in binary neutron star mergers and their multimessenger observables.
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Neural-Network-Based Variational Method in Nuclear Density Functional Theory: Application to the Extended Thomas-Fermi Model
Neural networks represent densities in a variational extended Thomas-Fermi model, yielding binding energies within 0.5% of prior ETF results and reproducing nuclear pasta phases.