Neural ODEs reproduce 2RDM dynamics from data only when three-particle cumulant correlations are strong, mapping the validity regime of cumulant expansions.
In Pro- ceedings of the 38th International Conference on Machine Le arning, PMLR, 139, pp 7829–7838
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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.
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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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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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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.