Estimation of Thermodynamic Observables in Lattice Field Theories with Deep Generative Models
This addresses a limitation in computational physics for researchers, offering a new approach to estimate thermodynamic observables that MCMC cannot handle directly.
The paper tackles the problem of estimating absolute free energy in lattice field theories, where traditional MCMC methods fail, by using deep generative models, demonstrating effectiveness in 2D φ⁴ theory with numerical comparisons.
In this work, we demonstrate that applying deep generative machine learning models for lattice field theory is a promising route for solving problems where Markov Chain Monte Carlo (MCMC) methods are problematic. More specifically, we show that generative models can be used to estimate the absolute value of the free energy, which is in contrast to existing MCMC-based methods which are limited to only estimate free energy differences. We demonstrate the effectiveness of the proposed method for two-dimensional $φ^4$ theory and compare it to MCMC-based methods in detailed numerical experiments.