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: 14 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

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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