News Approaches for Boltzmann Generators: Sampling Equilibrium States of Many-Body Systems

Boltzmann Generators are a novel rare-event sampling framework that combines invertible neural networks (normalizing flows) with statistical-mechanics based reweighting methods [1]. The basic idea is to train the invertible neural network to generate samples from a probability distribution similar to the target equilibrium distribution (e.g. canonical), and then reweight to the exact target density so as to generate asymptotically unbiased samples. Ways to incorporate symmetries of the energy function [2] and stochastic sampling steps in the neural network [3] have also been proposed. In this talk I will describe recent methodological developments and applications to the sampling of classical many-body systems.


[1] https://science.sciencemag.org/content/365/6457/eaaw1147
[2] https://arxiv.org/abs/2006.02425
[3] https://arxiv.org/abs/2002.06707