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| Main Author: | |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.19692 |
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| _version_ | 1866929366117646336 |
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| author | Jin, Xiao-Yong |
| author_facet | Jin, Xiao-Yong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_19692 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Neural Network Gauge Field Transformation for 4D SU(3) gauge fields Jin, Xiao-Yong High Energy Physics - Lattice 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. |
| title | Neural Network Gauge Field Transformation for 4D SU(3) gauge fields |
| topic | High Energy Physics - Lattice |
| url | https://arxiv.org/abs/2405.19692 |