IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v617y2023ics0378437123002212.html
   My bibliography  Save this article

Study of phase transition of Potts model with Domain Adversarial Neural Network

Author

Listed:
  • Chen, Xiangna
  • Liu, Feiyi
  • Chen, Shiyang
  • Shen, Jianmin
  • Deng, Weibing
  • Papp, Gábor
  • Li, Wei
  • Yang, Chunbin

Abstract

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.

Suggested Citation

  • Chen, Xiangna & Liu, Feiyi & Chen, Shiyang & Shen, Jianmin & Deng, Weibing & Papp, Gábor & Li, Wei & Yang, Chunbin, 2023. "Study of phase transition of Potts model with Domain Adversarial Neural Network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 617(C).
    • Handle: RePEc:eee:phsmap:v:617:y:2023:i:c:s0378437123002212
    DOI: 10.1016/j.physa.2023.128666
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437123002212
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2023.128666?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Qiyi He & Xiaolin Meng & Rong Qu & Ruijie Xi, 2020. "Machine Learning-Based Detection for Cyber Security Attacks on Connected and Autonomous Vehicles," Mathematics, MDPI, vol. 8(8), pages 1-20, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chen, Xiangna & Liu, Feiyi & Deng, Weibing & Chen, Shiyang & Shen, Jianmin & Papp, Gábor & Li, Wei & Yang, Chunbin, 2024. "Applications of Domain Adversarial Neural Network in phase transition of 3D Potts model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      Corrections

      All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:617:y:2023:i:c:s0378437123002212. See general information about how to correct material in RePEc.

      If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

      If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

      For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

      Please note that corrections may take a couple of weeks to filter through the various RePEc services.

      IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.