A McKean-Vlasov FBSDE generative model learns stochastic path laws that match observed terminal and time-marginal distributions via soft energy constraints rather than hard interpolation.
arXiv preprint arXiv:2211.01364 , year =
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
years
2026 5verdicts
UNVERDICTED 5representative citing papers
FES-FM applies reduced flow matching with a Hessian-derived prior to directly sample free energy surfaces in collective variable space, claiming lower computational cost and higher accuracy per unit time than standard methods.
Gaussian mixture models combined with multiple local linearizations solve nonlinear stochastic density steering and yield provably tighter approximation bounds than single-linearization baselines.
A forward-backward HJB duality computes the optimal stochastic transport control from easy forward relaxation trajectories alone, expressed as path-space free energy without backward simulation.
E-Bridge approximates low-cost geodesic trajectories in diffusion bridges for image restoration by using shorter time horizons, entropy-regularized starts mixing degraded images with noise, and consistency-model single-step mapping, achieving SOTA results with one or few steps.
citing papers explorer
-
Learning Generative Dynamics with Soft Law Constraints: A McKean-Vlasov FBSDE Approach
A McKean-Vlasov FBSDE generative model learns stochastic path laws that match observed terminal and time-marginal distributions via soft energy constraints rather than hard interpolation.
-
Free Energy Surface Sampling via Reduced Flow Matching
FES-FM applies reduced flow matching with a Hessian-derived prior to directly sample free energy surfaces in collective variable space, claiming lower computational cost and higher accuracy per unit time than standard methods.
-
Nonlinear Stochastic Density Steering via Gaussian Mixture Schrodinger Bridges and Multiple Linearizations
Gaussian mixture models combined with multiple local linearizations solve nonlinear stochastic density steering and yield provably tighter approximation bounds than single-linearization baselines.
-
Generative optimal transport via forward-backward HJB matching
A forward-backward HJB duality computes the optimal stochastic transport control from easy forward relaxation trajectories alone, expressed as path-space free energy without backward simulation.
-
Energy-oriented Diffusion Bridge for Image Restoration with Foundational Diffusion Models
E-Bridge approximates low-cost geodesic trajectories in diffusion bridges for image restoration by using shorter time horizons, entropy-regularized starts mixing degraded images with noise, and consistency-model single-step mapping, achieving SOTA results with one or few steps.