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Article Dans Une Revue Journal of Physics: Conference Series Année : 2022

Machine Learning the Square-Lattice Ising Model

Résumé

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.

Dates et versions

hal-03636160 , version 1 (09-04-2022)

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Burak Çivitcioğlu, Rudolf Römer, Andreas Honecker. Machine Learning the Square-Lattice Ising Model. Journal of Physics: Conference Series, 2022, 2207 (1), pp.012058. ⟨10.1088/1742-6596/2207/1/012058⟩. ⟨hal-03636160⟩
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