Practical applications of machine-learned flows on gauge fields
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910414396194816 |
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| author | Abbott, Ryan Albergo, Michael S. Boyda, Denis Hackett, Daniel C. Kanwar, Gurtej Romero-López, Fernando Shanahan, Phiala E. Urban, Julian M. |
| author_facet | Abbott, Ryan Albergo, Michael S. Boyda, Denis Hackett, Daniel C. Kanwar, Gurtej Romero-López, Fernando Shanahan, Phiala E. Urban, Julian M. |
| contents | Normalizing flows are machine-learned maps between different lattice theories which can be used as components in exact sampling and inference schemes. Ongoing work yields increasingly expressive flows on gauge fields, but it remains an open question how flows can improve lattice QCD at state-of-the-art scales. We discuss and demonstrate two applications of flows in replica exchange (parallel tempering) sampling, aimed at improving topological mixing, which are viable with iterative improvements upon presently available flows. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_11674 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Practical applications of machine-learned flows on gauge fields Abbott, Ryan Albergo, Michael S. Boyda, Denis Hackett, Daniel C. Kanwar, Gurtej Romero-López, Fernando Shanahan, Phiala E. Urban, Julian M. High Energy Physics - Lattice Statistical Mechanics Machine Learning Normalizing flows are machine-learned maps between different lattice theories which can be used as components in exact sampling and inference schemes. Ongoing work yields increasingly expressive flows on gauge fields, but it remains an open question how flows can improve lattice QCD at state-of-the-art scales. We discuss and demonstrate two applications of flows in replica exchange (parallel tempering) sampling, aimed at improving topological mixing, which are viable with iterative improvements upon presently available flows. |
| title | Practical applications of machine-learned flows on gauge fields |
| topic | High Energy Physics - Lattice Statistical Mechanics Machine Learning |
| url | https://arxiv.org/abs/2404.11674 |