论文标题
用深生成模型的晶格场理论中热力学可观察物的估计
Estimation of Thermodynamic Observables in Lattice Field Theories with Deep Generative Models
论文作者
论文摘要
在这项工作中，我们证明，将深层生成机学习模型应用于晶格场理论是解决马尔可夫链蒙特卡洛（MCMC）方法问题的有前途的途径。更具体地说，我们表明，生成模型可用于估计自由能的绝对值，这与现有的基于MCMC的方法相反，后者仅限于估计自由能差。我们证明了对二维$ ϕ^4 $理论的提议方法的有效性，并将其与基于MCMC的方法进行详细的数值实验进行了比较。
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.
