Work

Flow-based sampling for lattice field theories

Year: 2024

Type: conference-paper

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.

Source: International Symposium on Lattice Field Theory (LATTICE)

DOI: 10.22323/1.453.0114

Author: Gurtej Kanwar

Language: English

Topic: Quantum Chromodynamics and Particle Interactions

Subfield: Nuclear and High Energy Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: hybrid

Funders: National Science Foundation, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

Award: Non-Perturbative Quantization of Topological Excitations in Quantum Field Theory and Quantum Spin Systems

OpenAlex ID: W4403341563

FWCI: 9.301

Cites:

Cited by: 6

Related to: 10