NeuMC -一个用于晶格场理论的神经采样包

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX Pub Date : 2025-07-19 DOI:10.1016/j.softx.2025.102253

Piotr Białas , Piotr Korcyl , Tomasz Stebel , Dawid Zapolski

{"title":"NeuMC -一个用于晶格场理论的神经采样包","authors":"Piotr Białas , Piotr Korcyl , Tomasz Stebel , Dawid Zapolski","doi":"10.1016/j.softx.2025.102253","DOIUrl":null,"url":null,"abstract":"<div><div>We present the <span>NeuMC</span> software package 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 such as critical slowing down. Our package provides tools to create such samplers for two-dimensional field theories and is based on a popular <span>PyTorch</span> package.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"31 ","pages":"Article 102253"},"PeriodicalIF":2.4000,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"NeuMC — A package for neural sampling for lattice field theories\",\"authors\":\"Piotr Białas , Piotr Korcyl , Tomasz Stebel , Dawid Zapolski\",\"doi\":\"10.1016/j.softx.2025.102253\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>We present the <span>NeuMC</span> software package 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 such as critical slowing down. Our package provides tools to create such samplers for two-dimensional field theories and is based on a popular <span>PyTorch</span> package.</div></div>\",\"PeriodicalId\":21905,\"journal\":{\"name\":\"SoftwareX\",\"volume\":\"31 \",\"pages\":\"Article 102253\"},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2025-07-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"SoftwareX\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2352711025002201\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, SOFTWARE ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"SoftwareX","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352711025002201","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}

引用次数: 0

摘要

我们提出了neuc软件包，旨在促进晶格场理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罗模拟中越来越受欢迎，因为它可以有效地近似目标概率分布，可能减轻马尔可夫链蒙特卡罗方法的一些缺点，如临界减速。我们的包提供了为二维场论创建这样的采样器的工具，并且基于流行的PyTorch包。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

NeuMC — A package for neural sampling for lattice field theories

We present the NeuMC software package 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 such as critical slowing down. Our package provides tools to create such samplers for two-dimensional field theories and is based on a popular PyTorch package.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

SoftwareX COMPUTER SCIENCE, SOFTWARE ENGINEERING-

CiteScore

5.50

自引率

2.90%

发文量

184

审稿时长

9 weeks

期刊介绍： SoftwareX aims to acknowledge the impact of software on today''s research practice, and on new scientific discoveries in almost all research domains. SoftwareX also aims to stress the importance of the software developers who are, in part, responsible for this impact. To this end, SoftwareX aims to support publication of research software in such a way that: The software is given a stamp of scientific relevance, and provided with a peer-reviewed recognition of scientific impact; The software developers are given the credits they deserve; The software is citable, allowing traditional metrics of scientific excellence to apply; The academic career paths of software developers are supported rather than hindered; The software is publicly available for inspection, validation, and re-use. Above all, SoftwareX aims to inform researchers about software applications, tools and libraries with a (proven) potential to impact the process of scientific discovery in various domains. The journal is multidisciplinary and accepts submissions from within and across subject domains such as those represented within the broad thematic areas below: Mathematical and Physical Sciences; Environmental Sciences; Medical and Biological Sciences; Humanities, Arts and Social Sciences. Originating from these broad thematic areas, the journal also welcomes submissions of software that works in cross cutting thematic areas, such as citizen science, cybersecurity, digital economy, energy, global resource stewardship, health and wellbeing, etcetera. SoftwareX specifically aims to accept submissions representing domain-independent software that may impact more than one research domain.