LightGBM and other regressors achieve R^{2}≈0.6–0.7 under random CV on CCSN GW catalogues but collapse to worse-than-mean performance under Leave-One-EoS-Out validation, exposing a generalisation gap for unseen EoS families.
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The paper introduces neural-network trial wave functions for variational Monte Carlo, frames the variational method as unsupervised learning, and illustrates the approach on the Yukawa potential and hydrogen molecule.
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The Generalization Gap in Machine Learning EoS Inference from Core-Collapse Supernova Gravitational Waves
LightGBM and other regressors achieve R^{2}≈0.6–0.7 under random CV on CCSN GW catalogues but collapse to worse-than-mean performance under Leave-One-EoS-Out validation, exposing a generalisation gap for unseen EoS families.
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Introduction to the artificial neural network-based variational Monte Carlo method
The paper introduces neural-network trial wave functions for variational Monte Carlo, frames the variational method as unsupervised learning, and illustrates the approach on the Yukawa potential and hydrogen molecule.