A PINN approach learns galactic gravitational potentials from acceleration data, achieving sub-percent errors on simulations while outperforming analytic models and retaining interpretability via structured priors.
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Bayesian analysis of 163 open clusters finds a universal initial IMF slope of -2.29 with 0.17 scatter and minimal primordial mass segregation, followed by rapid internal mass segregation and later tidal MF flattening.
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Reconstructing Galactic Gravitational Potentials from Stellar Kinematics with Physics-Informed Neural Networks
A PINN approach learns galactic gravitational potentials from acceleration data, achieving sub-percent errors on simulations while outperforming analytic models and retaining interpretability via structured priors.
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Evolution of the stellar mass function in open clusters from a universal and unsegregated initial state
Bayesian analysis of 163 open clusters finds a universal initial IMF slope of -2.29 with 0.17 scatter and minimal primordial mass segregation, followed by rapid internal mass segregation and later tidal MF flattening.