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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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
Bayesian framework with active-learning surrogate for MESA models constrains ages and α_MLT from 38 main-sequence DEBs, recovering some α_MLT values below the solar calibration.
Presents a grid of 113 fast-rotating, chemically-homogeneous massive star models at Z=0.001 reaching core collapse with high angular momentum for use as supernova and GRB progenitors.
citing papers explorer
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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.
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Precision constraints on stellar physics from main sequence detached eclipsing binaries
Bayesian framework with active-learning surrogate for MESA models constrains ages and α_MLT from 38 main-sequence DEBs, recovering some α_MLT values below the solar calibration.
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A grid of fast-rotating, chemically-homogeneous, supernova and/or long-GRB progenitors
Presents a grid of 113 fast-rotating, chemically-homogeneous massive star models at Z=0.001 reaching core collapse with high angular momentum for use as supernova and GRB progenitors.