The critical temperature of the 2D-Ising model through Deep Learning Autoencoders

Understand

We investigate deep learning autoencoders for the unsupervised recognition of phase transitions in physical systems formulated on a lattice.

• We focus our investigation on the 2-dimensional ferromagnetic Ising model and then test the application of the autoencoder on the anti-ferromagnetic Ising model.
• We use spin configurations produced for the 2-dimensional ferromagnetic and anti-ferromagnetic Ising model in zero external magnetic field.
• For the ferromagnetic Ising model, we study numerically the relation between one latent variable extracted from the autoencoder to the critical temperature $T_c$.