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https://github.com/Anguswlx/CAN2XY
Code for recognizing the KT phase transition in classical 2D XY model by the Variational Autoregressive Networks
https://github.com/Anguswlx/CAN2XY
Last synced: 11 days ago
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Code for recognizing the KT phase transition in classical 2D XY model by the Variational Autoregressive Networks
- Host: GitHub
- URL: https://github.com/Anguswlx/CAN2XY
- Owner: Anguswlx
- License: mit
- Created: 2020-05-09T12:29:33.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2020-08-06T13:41:27.000Z (over 4 years ago)
- Last Synced: 2024-08-01T16:54:12.909Z (3 months ago)
- Language: Python
- Size: 23.4 KB
- Stars: 5
- Watchers: 2
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Recognizing the topological phase transition by the Continuous-mixture Autoregressive Networks
Cite this work as,
L. Wang, Y. Jiang, L. He, and K. Zhou, ArXiv:2005.04857 [Cond-Mat] (2020).
## Getting Started
The code requires Python >= 3.7 and PyTorch >= 1.2. You can configure on CPU machine and accelerate with a recent Nvidia GPU card.
Other requirements.
numpy==1.16.4
torch==1.1.0
torchvision==0.3.0
uncertainties==3.1.1## Running the tests
Run a small size example.
python3 main_xy.py --ham fm --lattice sqr --L 4 --beta 1 --net pixelcnn_xy --net_depth 3 --net_width 16 --bias --lr_schedule --beta_anneal 0.998 --clip_grad 1 --save_step 10 --visual_step 10 --save_sample --max_step 100 --cuda -1
## Authors
* **Lingxiao Wang** - *Construct codes and write the preprint paper* - [Homepage](https://sites.google.com/view/lingxiao)
* **Yin Jiang** - *Check codes and provide physics guidance*
* **Lianyi He** - *Provide physics guidance and polish the article*
* **Kai Zhou** - *Lead the project and complete the article.*## License
This project is licensed under the MIT License - see the LICENSE file for details