Flow-based sampling for lattice field theories

2024/01/02 by Gurtej Kanwar, Kanwar, Gurtej · 2 citations
Computer Science · Mathematics · Physics and Astronomy · #Advanced Data Storage Technologies #FOS: Physical sciences #High Energy Physics - Lattice (hep-lat) #Stochastic processes and statistical mechanics #Theoretical and Computational Physics

paper · pdf · doi:10.48550/arxiv.2401.01297

openalex publication_date 2024/01/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Critical slowing down and topological freezing severely hinder Monte Carlo sampling of lattice field theories as the continuum limit is approached. Recently, significant progress has been made in applying a class of generative machine learning models, known as "flow-based" samplers, to combat these issues. These generative samplers also enable promising practical improvements in Monte Carlo sampling, such as fully parallelized configuration generation. These proceedings review the progress towards this goal and future prospects of the method.

Cited by

• Particle Monte Carlo methods for Lattice Field Theory
• Efficient Identification of Critical Transitions via Flow Matching: A Scalable Generative Approach for Many-Body Systems

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