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.
and Ghahramani, Zoubin and Jaakkola, Tommi S
2 Pith papers cite this work. Polarity classification is still indexing.
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A Bayesian mixture model with product-multinomial likelihood clusters over 11,000 recurrent users from 220,000+ categorical trips in Venice, yielding eight latent mobility profiles.
citing papers explorer
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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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Beyond the Flow: A Bayesian Latent Clustering Framework for Shared Micro-mobility Users in Venice
A Bayesian mixture model with product-multinomial likelihood clusters over 11,000 recurrent users from 220,000+ categorical trips in Venice, yielding eight latent mobility profiles.