NuGNN applies a heterogeneous graph neural network to surrogate-solve a 690-isotope nuclear reaction network, achieving few-percent errors and reproducing final abundances where fully connected and Res-U-Net models fail.
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Relativistic transport model for beta-particles in homologously expanding kilonova ejecta, incorporating per-species atomic data, shows non-local deposition and escape lower thermalization efficiency with analytic prescriptions supplied for light-curve codes.
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NuGNN: a Graph Neural Network for Nuclear Reaction Network Equations
NuGNN applies a heterogeneous graph neural network to surrogate-solve a 690-isotope nuclear reaction network, achieving few-percent errors and reproducing final abundances where fully connected and Res-U-Net models fail.