English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Using the techniques of Neural Networks (NN), we study the three-dimensional(3D) 5-state ferromagnetic Potts model on the cubic lattice as well as thetwo-dimensional (2D) 3-state antiferromagnetic Potts model on the squarelattice. Unlike the conventional approach, here we follow the idea employed inAnn.~Phy.~391 (2018) 312-331. Specifically, instead of numerically generatingnumerous objects for the training, the whole or part of the theoretical groundstate configurations of the studied models are considered as the training sets.Remarkably, our investigation of these two models provides convincing evidencefor the effectiveness of the method of preparing training sets used in thisstudy. In particular, the results of the 3D model obtained here imply that theNN approach is as efficient as the traditional method since the signal of afirst order phase transition, namely tunneling between two channels, determinedby the NN method is as strong as that calculated with the Monte Carlotechnique. Furthermore, the outcomes associated with the considered 2D systemindicate even little partial information of the ground states can lead toconclusive results regarding the studied phase transition. The achievementsreached in our investigation demonstrate that the performance of NN, usingcertain amount of the theoretical ground state configurations as the trainingsets, is impressive.

A Neural Networks study of the phase transitions of Potts model