Neural ODEs reproduce 2RDM dynamics from data only when three-particle cumulant correlations are strong, mapping the validity regime of cumulant expansions.
Neural Rough Differential Equations for Long Time Series
4 Pith papers cite this work, alongside 26 external citations. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
A Latent NCDE-based continuous-time probabilistic corrector wrapped around deterministic physics propagators like GMAT improves forecast accuracy and produces sharp calibrated full-covariance uncertainty estimates on real CDDIS data for 2-4 day horizons.
The paper proposes the ANJD flow and AVNSG operator to generate càdlàg trajectories via sequential MMD-gradient descent in Marcus-signature RKHS with generalisation bounds.
ARL lifts states into signature-augmented manifolds and employs self-consistent proxies of future path-laws to enable deterministic expected-return evaluation while preserving contraction mappings in jump-diffusion environments.
citing papers explorer
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Capturing reduced-order quantum many-body dynamics out of equilibrium via neural ordinary differential equations
Neural ODEs reproduce 2RDM dynamics from data only when three-particle cumulant correlations are strong, mapping the validity regime of cumulant expansions.
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Continuous-Time Probabilistic Correctors for Uncertainty-Aware Physics-Based Spacecraft Trajectory Forecasting
A Latent NCDE-based continuous-time probabilistic corrector wrapped around deterministic physics propagators like GMAT improves forecast accuracy and produces sharp calibrated full-covariance uncertainty estimates on real CDDIS data for 2-4 day horizons.
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Generative Path-Law Jump-Diffusion: Sequential MMD-Gradient Flows and Generalisation Bounds in Marcus-Signature RKHS
The paper proposes the ANJD flow and AVNSG operator to generate càdlàg trajectories via sequential MMD-gradient descent in Marcus-signature RKHS with generalisation bounds.
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Anticipatory Reinforcement Learning: From Generative Path-Laws to Distributional Value Functions
ARL lifts states into signature-augmented manifolds and employs self-consistent proxies of future path-laws to enable deterministic expected-return evaluation while preserving contraction mappings in jump-diffusion environments.