{"title":"Diffusion reconstruction for the diluted Ising model.","authors":"Stefano Bae, Enzo Marinari, Federico Ricci-Tersenghi","doi":"10.1103/PhysRevE.111.L023301","DOIUrl":null,"url":null,"abstract":"<p><p>Diffusion-based generative models are machine learning models that use diffusion processes to learn the probability distribution of high-dimensional data. In recent years they have become extremely successful in generating multimedia content. However, it is still unknown whether such models can be used to generate high-quality datasets of physical models. In this work we use a Landau-Ginzburg-like diffusion model to infer the distribution of a two-dimensional bond-diluted Ising model. Our approach is simple and effective, and we show that the generated samples correctly reproduce the statistical and critical properties of the physical model.</p>","PeriodicalId":48698,"journal":{"name":"Physical Review E","volume":"111 2","pages":"L023301"},"PeriodicalIF":2.2000,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physical Review E","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.1103/PhysRevE.111.L023301","RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PHYSICS, FLUIDS & PLASMAS","Score":null,"Total":0}
引用次数: 0
Abstract
Diffusion-based generative models are machine learning models that use diffusion processes to learn the probability distribution of high-dimensional data. In recent years they have become extremely successful in generating multimedia content. However, it is still unknown whether such models can be used to generate high-quality datasets of physical models. In this work we use a Landau-Ginzburg-like diffusion model to infer the distribution of a two-dimensional bond-diluted Ising model. Our approach is simple and effective, and we show that the generated samples correctly reproduce the statistical and critical properties of the physical model.
期刊介绍:
Physical Review E (PRE), broad and interdisciplinary in scope, focuses on collective phenomena of many-body systems, with statistical physics and nonlinear dynamics as the central themes of the journal. Physical Review E publishes recent developments in biological and soft matter physics including granular materials, colloids, complex fluids, liquid crystals, and polymers. The journal covers fluid dynamics and plasma physics and includes sections on computational and interdisciplinary physics, for example, complex networks.