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arXiv · 2111.13413

Machine Learning the Square-Lattice Ising Model

Burak Çivitcioğlu·Rudolf A. Römer·Andreas Honecker
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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.

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Burak Çivitcioğlu, Rudolf A. Römer, Andreas Honecker. 2021-11-26. Machine Learning the Square-Lattice Ising Model. https://doi.org/10.1088/1742-6596%2F2207%2F1%2F012058

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