Phase Transition Study meets Machine Learning
Fuente:
arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866929198019379200 |
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| author | Ma, Yu-Gang Pang, Long-Gang Wang, Rui Zhou, Kai |
| author_facet | Ma, Yu-Gang Pang, Long-Gang Wang, Rui Zhou, Kai |
| contents | In recent years, machine learning (ML) techniques have emerged as powerful tools for studying many-body complex systems, and encompassing phase transitions in various domains of physics. This mini review provides a concise yet comprehensive examination of the advancements achieved in applying ML to investigate phase transitions, with a primary focus on those involved in nuclear matter studies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_07274 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Phase Transition Study meets Machine Learning Ma, Yu-Gang Pang, Long-Gang Wang, Rui Zhou, Kai Nuclear Theory High Energy Physics - Phenomenology In recent years, machine learning (ML) techniques have emerged as powerful tools for studying many-body complex systems, and encompassing phase transitions in various domains of physics. This mini review provides a concise yet comprehensive examination of the advancements achieved in applying ML to investigate phase transitions, with a primary focus on those involved in nuclear matter studies. |
| title | Phase Transition Study meets Machine Learning |
| topic | Nuclear Theory High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2311.07274 |