Non-Perturbative Trivializing Flows for Lattice Gauge Theories

Fuente: arXiv
Saved in:
Bibliographic Details
Main Authors: Gerdes, Mathis, de Haan, Pim, Bondesan, Roberto, Cheng, Miranda C. N.
Format: Preprint
Published: 2024
Subjects:
Online Access:
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1866911326869127168
author Gerdes, Mathis
de Haan, Pim
Bondesan, Roberto
Cheng, Miranda C. N.
author_facet Gerdes, Mathis
de Haan, Pim
Bondesan, Roberto
Cheng, Miranda C. N.
contents Continuous normalizing flows are known to be highly expressive and flexible, which allows for easier incorporation of large symmetries and makes them a powerful computational tool for lattice field theories. Building on previous work, we present a general continuous normalizing flow architecture for matrix Lie groups that is equivariant under group transformations. We apply this to lattice gauge theories in two dimensions as a proof of principle and demonstrate competitive performance, showing its potential as a tool for future lattice computations.
format Preprint
id arxiv_https___arxiv_org_abs_2410_13161
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Non-Perturbative Trivializing Flows for Lattice Gauge Theories
Gerdes, Mathis
de Haan, Pim
Bondesan, Roberto
Cheng, Miranda C. N.
High Energy Physics - Lattice
Statistical Mechanics
Machine Learning
High Energy Physics - Theory
Continuous normalizing flows are known to be highly expressive and flexible, which allows for easier incorporation of large symmetries and makes them a powerful computational tool for lattice field theories. Building on previous work, we present a general continuous normalizing flow architecture for matrix Lie groups that is equivariant under group transformations. We apply this to lattice gauge theories in two dimensions as a proof of principle and demonstrate competitive performance, showing its potential as a tool for future lattice computations.
title Non-Perturbative Trivializing Flows for Lattice Gauge Theories
topic High Energy Physics - Lattice
Statistical Mechanics
Machine Learning
High Energy Physics - Theory
url https://arxiv.org/abs/2410.13161