Speaker
Description
Sampling with flows and diffusion models has emerged as a promising alternative to MCMC in lattice field theory. A central obstacle to their practical adoption is the degradation of sample quality as the lattice volume grows, and identifying a scalable prescription for applying deep generative models to the lattice setting is still an open problem. We adapt and extend a framework based on non-equilibrium transport sampling (NETS) as a continuous-time generalization of annealed importance sampling. Our approach enables diffusion-based generation that remains asymptotically unbiased by accumulating dynamical reweighting factors along the interpolating trajectory at inference time, with the effective sample size systematically refinable through tunable post-training parameters and no retraining. We illustrate our method by training energy-based diffusion models in the context of scalar field theory and the ${\rm O}(3)$ sigma model, demonstrating monotonic improvement of the ESS and reduction in the variance of the importance weights with the number of integration steps and strength of the stochastic transport term.