提出新蒙特卡洛方法，高效求解场变换中的流场，缓解采样难题。
A Monte Carlo estimator of flow fields for sampling and noise problems
- 用耦合朗之万噪声构造蒙特卡洛估计器，降低积分噪声
- 在U(1)和SU(N)模型中验证，有效解决信号-噪声问题
- 可直接用于采样或生成无偏机器学习训练数据
学习到的场变换可缓解格点场论中普遍存在的临界慢化和信噪比问题。在退火分布序列背景下，场变换由精确求解局部输运问题的流场定义。本文介绍一种新的蒙特卡洛方法来评估这些流场，可用于直接采样，或作为机器学习方法的无偏训练数据生成手段。通过采用耦合朗之万噪声定义蒙特卡洛估计器，显著降低了所需积分中的统计噪声。方法演示包括U(1)输运问题和SU(N)胶球关联函数。
原文摘要 · Abstract (English)
Learned field transformations may help address ubiquitous critical slowing down and signal-to-noise problems in lattice field theory. In the context of an annealed sequence of distributions, field transformations are defined by integrating flow fields that exactly solve a local transport problem. These proceedings discuss a new Monte Carlo approach to evaluating these flow fields, which can then be used directly in such contexts or as a means of generating unbiased training data for machine learning approaches. By defining the Monte Carlo estimator using coupled Langevin noise, the statistical noise in the required integrals is significantly mitigated. Demonstrations of the method include a U(1) transport problem and an SU(N) glueball correlator.
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