Neural Network Gauge Field Transformation for 4D SU(3) gauge fields

We construct neural networks that work for any Lie group and maintain gauge covariance, enabling smooth, invertible gauge field transformations. We implement these transformations for 4D SU(3) lattice gauge fields and explore their use in HMC. We focus on developing loss functions and optimizing the transformations. We show the effects on HMC's molecular dynamics and discuss the scalability of the approach.

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Publication Details

Published
2024-10-11
DOI
https://doi.org/10.22323/1.453.0040
Primary Topic
Computational Physics and Python Applications
Type
article
Field-Weighted Citation Impact
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article

Neural Network Gauge Field Transformation for 4D SU(3) gauge fields

Xiao-Yong Jin
Computational Physics and Python Applications
article

Neural Network Gauge Field Transformation for 4D SU(3) gauge fields

Xiao-Yong Jin
article en

Abstract

We construct neural networks that work for any Lie group and maintain gauge covariance, enabling smooth, invertible gauge field transformations. We implement these transformations for 4D SU(3) lattice gauge fields and explore their use in HMC. We focus on developing loss functions and optimizing the transformations. We show the effects on HMC's molecular dynamics and discuss the scalability of the approach.

Openalex Percentile: Top 84%
Computational Physics and Python Applications
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Neural Network Gauge Field Transformation for 4D SU(3) gauge fields — Xiao-Yong Jin · (2024) | TGRS Research Map | TGRS