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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2404.14909 |
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| _version_ | 1866914766659780608 |
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| author | Trenta, Alessandro Bacciu, Davide Cossu, Andrea Ferrero, Pietro |
| author_facet | Trenta, Alessandro Bacciu, Davide Cossu, Andrea Ferrero, Pietro |
| contents | We develop MultiSTOP, a Reinforcement Learning framework for solving functional equations in physics. This new methodology produces actual numerical solutions instead of bounds on them. We extend the original BootSTOP algorithm by adding multiple constraints derived from domain-specific knowledge, even in integral form, to improve the accuracy of the solution. We investigate a particular equation in a one-dimensional Conformal Field Theory. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_14909 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MultiSTOP: Solving Functional Equations with Reinforcement Learning Trenta, Alessandro Bacciu, Davide Cossu, Andrea Ferrero, Pietro Machine Learning High Energy Physics - Theory We develop MultiSTOP, a Reinforcement Learning framework for solving functional equations in physics. This new methodology produces actual numerical solutions instead of bounds on them. We extend the original BootSTOP algorithm by adding multiple constraints derived from domain-specific knowledge, even in integral form, to improve the accuracy of the solution. We investigate a particular equation in a one-dimensional Conformal Field Theory. |
| title | MultiSTOP: Solving Functional Equations with Reinforcement Learning |
| topic | Machine Learning High Energy Physics - Theory |
| url | https://arxiv.org/abs/2404.14909 |