论文标题
晶格量规理论的基于流动的基于流动的抽样
Equivariant flow-based sampling for lattice gauge theory
论文作者
论文摘要
我们为构造规范不变的晶格规定理论定义了一类机器学习的基于流量的采样算法。我们证明了该框架在两个时空维度中应用于U（1）量规理论，并发现参数空间中的临界点接近临界点的方法是与更传统的抽样程序（如混合蒙特卡洛和热浴）相比，在采样拓扑数量上的效率更高。
We define a class of machine-learned flow-based sampling algorithms for lattice gauge theories that are gauge-invariant by construction. We demonstrate the application of this framework to U(1) gauge theory in two spacetime dimensions, and find that near critical points in parameter space the approach is orders of magnitude more efficient at sampling topological quantities than more traditional sampling procedures such as Hybrid Monte Carlo and Heat Bath.
