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Critical Temperature from Unsupervised Deep Learning Autoencoders
2022
Zenodo
We discuss deep learning autoencoders for the unsupervised recognition of phase transitions in physical systems formulated on a lattice. We elaborate on the applicability and limitations of this deep learning model in terms of extracting the relevant physics. Although our results are shown in the context of 2D, 3D and 4D Ising models as well as 2D Potts model, we focus on the analysis of the critical quantities at 2D (anti)ferromagnetic Ising Model. We define as a quasi-order parameter the
doi:10.5281/zenodo.6405399
fatcat:bcqaehqn55ed7jliqrzicrcq6y