Backward SDEs-based Diffusion for Physics-Constrained Generation
A practical neural BSDE solver is developed that composes arbitrary pretrained diffusion priors with domain constraints without modifying the score-defined coefficients, producing an anchored prior state that enables neighborhood sampling for uncertainty characterization and establishes existence and uniqueness of the...