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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We investigate the application of deep learning techniques employing theconditional variational autoencoders for semi-supervised learning of latentparameters to describe phase transition in the two-dimensional (2D)ferromagnetic Ising model and the two-dimensional XY model. For both models, weutilize spin configurations generated using the Wolff algorithms below andabove the critical temperatures. For the 2D Ising model we find the latentparameter of conditional variational autoencoders is correlated to the knownorder parameter of magnetization more efficiently than their correspondence invariational autoencoders used previously. It can also clearly identify therestoration of the $\mathbb{Z}_2$ symmetry beyond the critical point. Thecritical temperature extracted from the latent parameter at larger lattices arefound to be approaching its correct value. Similarly, for the 2D XY model, wefind our chosen network with the latent representation of conditionalvariational autoencoders is equally capable of separating the two phasesbetween the high and low temperatures, again at the correct criticaltemperature with reasonable accuracy. Together these results show that thelatent representation of conditional variational autoencoders can be employedefficiently to identify the phases of condensed matter systems, without theirprior knowledge.

Semi-supervised learning of order parameter in 2D Ising and XY models using Conditional Variational Autoencoders