Unsupervised learning of site percolation based on shuffled
  configurations
Dian Xu Shanshan Wang Feng Gao Wei Li Jianmin Shen
In the field of statistical physics, machine learning has gained significant
popularity and has achieved remarkable results in recent studies on phase
transitions.In this paper, we apply Principal Component Analysis (PCA) and
Autoencoder(AE) based on Unsupervised learning to study the various
configurations of the percolation model in equilibrium phase transition. In
certain phase transition models, such as the DP model in non-equilibrium phase
transitions, the order parameter is particle density. However, in some other
phase transition models, such as the percolation model, it is not. This study
involved randomizing and selecting percolation graphs to be used as input for a
neural network, and analyzed the obtained results, indicating that the outputs
of the single latent variable of AE and the first principal component of PCA
are signals related to particle density.