论文标题
晶格标量场理论中蒙特卡洛采样的条件归一流流量
Conditional Normalizing flow for Monte Carlo sampling in lattice scalar field theory
论文作者
论文摘要
由于样品之间的高相关性，在晶格场理论的临界区域，晶格构型的蒙特卡洛采样成本非常高。本文提出了一个有条件的归一化流（C-NF）模型，用于在临界区域采样晶格配置，以解决关键减慢的问题。我们使用混合蒙特卡洛（HMC）生成的样品在非关键区域中生成的样品训练C-NF模型。训练有素的C-NF模型在关键区域使用，以构建具有可忽略的自相关的晶格样品链。 C-NF模型用于插值和外推到晶格理论的关键区域。我们提出的方法使用1+1维标量$ ϕ^4 $理论评估。这种方法可以为关键区域的许多参数值构建晶格合奏，从而避免临界减速来降低模拟成本。
The cost of Monte Carlo sampling of lattice configurations is very high in the critical region of lattice field theory due to the high correlation between the samples. This paper suggests a Conditional Normalizing Flow (C-NF) model for sampling lattice configurations in the critical region to solve the problem of critical slowing down. We train the C-NF model using samples generated by Hybrid Monte Carlo (HMC) in non-critical regions with low simulation costs. The trained C-NF model is employed in the critical region to build a Markov chain of lattice samples with negligible autocorrelation. The C-NF model is used for both interpolation and extrapolation to the critical region of lattice theory. Our proposed method is assessed using the 1+1-dimensional scalar $ϕ^4$ theory. This approach enables the construction of lattice ensembles for many parameter values in the critical region, which reduces simulation costs by avoiding the critical slowing down.
