HIN-LRI augments a low-regularity integrator with a latent-manifold neural correction trained end-to-end on trajectory error to improve accuracy on nonlinear dispersive equations with rough data.
Jiachuan Cao, Buyang Li, and Yanping Lin
2 Pith papers cite this work, alongside 47 external citations. Polarity classification is still indexing.
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Derives the resonance-based midpoint rule as a symplectic scheme for stochastic NLS and analyzes its convergence in low regularity.
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Hybrid Iterative Neural Low-Regularity Integrator for Nonlinear Dispersive Equations
HIN-LRI augments a low-regularity integrator with a latent-manifold neural correction trained end-to-end on trajectory error to improve accuracy on nonlinear dispersive equations with rough data.
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Low regularity symplectic schemes for stochastic NLS
Derives the resonance-based midpoint rule as a symplectic scheme for stochastic NLS and analyzes its convergence in low regularity.