论文标题
神经网络现场转换及其在HMC中的应用
Neural Network Field Transformation and Its Application in HMC
论文作者
论文摘要
我们提出了谎言组不可知论和量规协方差网络的通用结构，并引入了限制，以使神经网络连续可区分和可逆。我们结合了此类神经网络，并建立适合混合蒙特卡洛（HMC）的量规场变换。我们使用HMC通过神经网络参数量规变换在变换空间中的晶格量规配置进行样品。与直接HMC采样相比，在一系列耦合和晶格大小的范围内，通过2D U（1）纯仪表系统进行了测试，神经网络转化的HMC（NTHMC）生成了衡量指数的Markov链，并改善了拓扑电荷的隧道，同时允许更少的力量计算，以减少力量计算，以增加晶状体的增加。
We propose a generic construction of Lie group agnostic and gauge covariant neural networks, and introduce constraints to make the neural networks continuous differentiable and invertible. We combine such neural networks and build gauge field transformations that is suitable for Hybrid Monte Carlo (HMC). We use HMC to sample lattice gauge configurations in the transformed space by the neural network parameterized gauge field transformations. Tested with 2D U(1) pure gauge systems at a range of couplings and lattice sizes, compared with direct HMC sampling, the neural network transformed HMC (NTHMC) generates Markov chains of gauge configurations with improved tunneling of topological charges, while allowing less force calculations as the lattice coupling increases.
