NeuMC工具包助力神经采样在格点场论中的应用
NeuMC -- a package for neural sampling for lattice field theories
- 基于PyTorch构建，支持生成式模型的神经采样器开发
- 专为二维格点场论设计，可高效逼近目标概率分布
- 适合从事量子场论与机器学习交叉研究者使用
我们介绍了一个名为\texttt{NeuMC}的软件包，基于\pytorch，旨在促进格点场论中神经采样器的研究。基于归一化流的神经采样器在蒙特卡洛模拟中日益流行，因其能有效逼近目标概率分布，可能缓解马尔可夫链蒙特卡洛方法的部分局限性。本工具包提供创建二维场论神经采样器的实用工具。
原文摘要 · Abstract (English)
We present the \texttt{NeuMC} software package, based on \pytorch, aimed at facilitating the research on neural samplers in lattice field theories. Neural samplers based on normalizing flows are becoming increasingly popular in the context of Monte-Carlo simulations as they can effectively approximate target probability distributions, possibly alleviating some shortcomings of the Markov chain Monte-Carlo methods. Our package provides tools to create such samplers for two-dimensional field theories.
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