Work

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

Year: 2024

Type: conference-paper

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.

Source: International Symposium on Lattice Field Theory (LATTICE)

DOI: 10.22323/1.453.0040

Author: Xiao-yong Jin

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: U.S. Department of Energy, Office of Science, National Nuclear Security Administration, Advanced Scientific Computing Research, Argonne National Laboratory

Awards: G102163711, G107546177, G1193633358, G1313983767, G1664757510, G1796307089, G2287611682, G262962599, G3075337988, G3318903820, G3886122786, G498139845, G5692331544, G6848031779, G6918803902, G878601127, G969889393, G993149817

OpenAlex ID: W4403341606

FWCI:

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