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Sampling topological quantities in the Monte Carlo simulation of lattice gauge theory becomes challenging as we approach the continuum limit (a→0) of the theory. In this work, we introduce a conditional normalizing flow model to sample U(1) gauge theory in two dimensions, aiming to mitigate the impact of topological freezing when dealing with smaller values of the U(1) bare coupling.
Abstract The traditional Markov chain Monte Carlo (MCMC) suffers from the problem of critical slowing down. Generative machine learning methods, such as normalizing flows, offer a promising method to speed up MCMC simulations, especially in critical regions of lattice field theory. However, training these models for different parameter values in the critical region is inefficient.
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