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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.01919 |
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| _version_ | 1866916506792624128 |
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| author | Habibi, Diaa E. Aarts, Gert Wang, Lingxiao Zhou, Kai |
| author_facet | Habibi, Diaa E. Aarts, Gert Wang, Lingxiao Zhou, Kai |
| contents | The probability distribution effectively sampled by a complex Langevin process for theories with a sign problem is not known a priori and notoriously hard to understand. Diffusion models, a class of generative AI, can learn distributions from data. In this contribution, we explore the ability of diffusion models to learn the distributions created by a complex Langevin process. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_01919 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Diffusion models learn distributions generated by complex Langevin dynamics Habibi, Diaa E. Aarts, Gert Wang, Lingxiao Zhou, Kai High Energy Physics - Lattice Machine Learning The probability distribution effectively sampled by a complex Langevin process for theories with a sign problem is not known a priori and notoriously hard to understand. Diffusion models, a class of generative AI, can learn distributions from data. In this contribution, we explore the ability of diffusion models to learn the distributions created by a complex Langevin process. |
| title | Diffusion models learn distributions generated by complex Langevin dynamics |
| topic | High Energy Physics - Lattice Machine Learning |
| url | https://arxiv.org/abs/2412.01919 |