Reducing autocorrelation times in lattice simulations with generative adversarial networks - Urban, Julian M.; Pawlowski, Jan M. (Arxiv, 2018)
Short autocorrelation times are essential for a reliable error assessment in Monte Carlo simulations of lattice systems. A generative adversarial network (GAN) can provide independent samples, thereby eliminating autocorrelations in the Markov chain. We address the question of statistical accuracy by implementing GANs as an overrelaxation step, incorporated into a traditional hybrid Monte Carlo algorithm. This allows for a sensible numerical assessment of ergodicity and consistency. Results for scalar $φ^4$-theory in two dimensions are presented. We achieve a significant reduction of autocorrelations while accurately reproducing the correct statistics. We discuss possible improvements as well as solutions to persisting issues and outline strategies towards the application to gauge theory and critical slowing down.