Applications of Domain Adversarial Neural Network in phase transition of 3D Potts model
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866917592674861056 |
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| author | Chen, Xiangna Liu, Feiyi Deng, Weibing Chen, Shiyang Shen, Jianmin Papp, Gabor Li, Wei Yang, Chunbin |
| author_facet | Chen, Xiangna Liu, Feiyi Deng, Weibing Chen, Shiyang Shen, Jianmin Papp, Gabor Li, Wei Yang, Chunbin |
| contents | Machine learning techniques exhibit significant performance in discriminating different phases of matter and provide a new avenue for studying phase transitions. We investigate the phase transitions of three dimensional $q$-state Potts model on cubic lattice by using a transfer learning approach, Domain Adversarial Neural Network (DANN). With the unique neural network architecture, it could evaluate the high-temperature (disordered) and low-temperature (ordered) phases, and identify the first and second order phase transitions. Meanwhile, by training the DANN with a few labeled configurations, the critical points for $q=2,3,4$ and $5$ can be predicted with high accuracy, which are consistent with those of the Monte Carlo simulations. These findings would promote us to learn and explore the properties of phase transitions in high-dimensional systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_02479 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Applications of Domain Adversarial Neural Network in phase transition of 3D Potts model Chen, Xiangna Liu, Feiyi Deng, Weibing Chen, Shiyang Shen, Jianmin Papp, Gabor Li, Wei Yang, Chunbin Computational Physics Statistical Mechanics Machine learning techniques exhibit significant performance in discriminating different phases of matter and provide a new avenue for studying phase transitions. We investigate the phase transitions of three dimensional $q$-state Potts model on cubic lattice by using a transfer learning approach, Domain Adversarial Neural Network (DANN). With the unique neural network architecture, it could evaluate the high-temperature (disordered) and low-temperature (ordered) phases, and identify the first and second order phase transitions. Meanwhile, by training the DANN with a few labeled configurations, the critical points for $q=2,3,4$ and $5$ can be predicted with high accuracy, which are consistent with those of the Monte Carlo simulations. These findings would promote us to learn and explore the properties of phase transitions in high-dimensional systems. |
| title | Applications of Domain Adversarial Neural Network in phase transition of 3D Potts model |
| topic | Computational Physics Statistical Mechanics |
| url | https://arxiv.org/abs/2312.02479 |