Document Type

Article

Publication Date

January 2019

Publication Title

Journal of Physics: Conference Series

Volume

1290

Issue Number

1

DOI

10.1088/1742-6596/1290/1/012006

Disciplines

Astrophysics and Astronomy | Physical Sciences and Mathematics | Physics

Abstract

Machine learning techniques have been widely used in the study of strongly correlated systems in recent years. Here, we review some applications to classical and quantum many-body systems and present results from an unsupervised machine learning technique, the principal component analysis, employed to identify the finite-temperature phase transition of the three-dimensional Fermi-Hubbard model to the antiferromagnetically ordered state. We find that this linear method can capture the phase transition as well as other more complicated and nonlinear counterparts.

Comments

This article was published in Journal of Physics: Conference Series, volume 1290, issue 1, 2019 and can also be found at this link.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

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