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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2308.08615 |
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| _version_ | 1866911623799635968 |
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| author | Faraz, Ali Singha, Ankur Chakrabarti, Dipankar Nakajima, Shinichi Arora, Vipul |
| author_facet | Faraz, Ali Singha, Ankur Chakrabarti, Dipankar Nakajima, Shinichi Arora, Vipul |
| contents | The Heatbath Algorithm is commonly used for sampling in local lattice field theories, but performing exact updates or sampling from the local density is challenging when dealing with continuous variables. Heatbath methods rely on rejection-based sampling at each site, which can suffer from low acceptance rates if the proposal distribution is not optimally chosen a nontrivial task. In this work, we propose a novel, straightforward approach for generating proposals at each lattice site for the phi4 and XY models using generative AI models. This method learns a conditional local distribution, without requiring training samples from the target, conditioned on both neighboring sites and action parameter values. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_08615 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Improvement of Heatbath Algorithm in LFT using Generative models Faraz, Ali Singha, Ankur Chakrabarti, Dipankar Nakajima, Shinichi Arora, Vipul Computational Physics Disordered Systems and Neural Networks Statistical Mechanics High Energy Physics - Lattice The Heatbath Algorithm is commonly used for sampling in local lattice field theories, but performing exact updates or sampling from the local density is challenging when dealing with continuous variables. Heatbath methods rely on rejection-based sampling at each site, which can suffer from low acceptance rates if the proposal distribution is not optimally chosen a nontrivial task. In this work, we propose a novel, straightforward approach for generating proposals at each lattice site for the phi4 and XY models using generative AI models. This method learns a conditional local distribution, without requiring training samples from the target, conditioned on both neighboring sites and action parameter values. |
| title | Improvement of Heatbath Algorithm in LFT using Generative models |
| topic | Computational Physics Disordered Systems and Neural Networks Statistical Mechanics High Energy Physics - Lattice |
| url | https://arxiv.org/abs/2308.08615 |