Study of phase transition of Potts model with Domain Adversarial Neural Network
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2022
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866914683977465856 |
|---|---|
| author | Chen, Xiangna Liu, Feiyi Chen, Shiyang Shen, Jianmin Deng, Weibing Papp, Gabor Li, Wei Yang, Chunbin |
| author_facet | Chen, Xiangna Liu, Feiyi Chen, Shiyang Shen, Jianmin Deng, Weibing Papp, Gabor Li, Wei Yang, Chunbin |
| contents | A transfer learning method, Domain Adversarial Neural Network (DANN), is introduced to study the phase transition of two-dimensional q-state Potts model. With the DANN, we only need to choose a few labeled configurations automatically as input data, then the critical points can be obtained after training the algorithm. By an additional iterative process, the critical points can be captured to comparable accuracy to Monte Carlo simulations as we demonstrate it for q = 3, 4, 5, 7 and 10. The type of phase transition (first or second-order) is also determined at the same time. Meanwhile, for the second-order phase transition at q=3, we can calculate the critical exponent $ν$ by data collapse. Furthermore, compared to the traditional supervised learning, we found the DANN to be more accurate with lower cost. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2209_03572 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Study of phase transition of Potts model with Domain Adversarial Neural Network Chen, Xiangna Liu, Feiyi Chen, Shiyang Shen, Jianmin Deng, Weibing Papp, Gabor Li, Wei Yang, Chunbin Statistical Mechanics Computational Physics A transfer learning method, Domain Adversarial Neural Network (DANN), is introduced to study the phase transition of two-dimensional q-state Potts model. With the DANN, we only need to choose a few labeled configurations automatically as input data, then the critical points can be obtained after training the algorithm. By an additional iterative process, the critical points can be captured to comparable accuracy to Monte Carlo simulations as we demonstrate it for q = 3, 4, 5, 7 and 10. The type of phase transition (first or second-order) is also determined at the same time. Meanwhile, for the second-order phase transition at q=3, we can calculate the critical exponent $ν$ by data collapse. Furthermore, compared to the traditional supervised learning, we found the DANN to be more accurate with lower cost. |
| title | Study of phase transition of Potts model with Domain Adversarial Neural Network |
| topic | Statistical Mechanics Computational Physics |
| url | https://arxiv.org/abs/2209.03572 |