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

We employ unsupervised machine learning techniques to learn latent parameterswhich best describe states of the two-dimensional Ising model and thethree-dimensional XY model. These methods range from principal componentanalysis to artificial neural network based variational autoencoders. Thestates are sampled using a Monte-Carlo simulation above and below the criticaltemperature. We find that the predicted latent parameters correspond to theknown order parameters. The latent representation of the states of the modelsin question are clustered, which makes it possible to identify phases withoutprior knowledge of their existence or the underlying Hamiltonian. Furthermore,we find that the reconstruction loss function can be used as a universalidentifier for phase transitions.

Unsupervised learning of phase transitions: from principal component analysis to variational autoencoders