A conditional graph neural network serves as an accurate and fast surrogate for semi-analytic galaxy formation models, predicting key properties across cosmic time.
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Single-seed CRPS estimates in limited-data BDL show high variance and peaks for heteroscedastic methods, with local variance correlating above 0.96 to single-seed error.
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A graph-based Neural Network surrogate model for accelerating semi-analytical model of galaxy formation and evolution
A conditional graph neural network serves as an accurate and fast surrogate for semi-analytic galaxy formation models, predicting key properties across cosmic time.
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A Tale of Two Variances: When Single-Seed Benchmarks Fail in Bayesian Deep Learning
Single-seed CRPS estimates in limited-data BDL show high variance and peaks for heteroscedastic methods, with local variance correlating above 0.96 to single-seed error.