Machine Learning the Square-Lattice Ising Model
Burak Çivitcioğlu, Rudolf A. Römer, A. Honecker
Abstract Recently, machine-learning methods have been shown to be successful in identifying and classifying different phases of the square-lattice Ising model. We study the performance and limits of classification and regression models. In particular, we investigate how accurately the correlation length, energy and magnetisation can be recovered from a given configuration. We find that a supervised learning study of a regression model yields good predictions for magnetisation and energy, and acceptable predictions for the correlation length.
arXiv:2111.13413•Journal of Physics Conference Series•Mar 1, 2022•7 citations