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arXiv · 1912.09005

Optimal Real-Space Renormalization-Group Transformations with Artificial Neural Networks

Jui-Hui Chung·Ying-Jer Kao
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Abstract

We introduce a general method for optimizing real-space renormalization-group transformations to study the critical properties of a classical system. The scheme is based on minimizing the Kullback-Leibler divergence between the distribution of the system and the normalized normalizing factor of the transformation parametrized by a restricted Boltzmann machine. We compute the thermal critical exponent of the two-dimensional Ising model using the trained optimal projector and obtain a very accurate thermal critical exponent $y_t=1.0001(11)$ after the first step of the transformation.

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BibTeXRIS

Jui-Hui Chung, Ying-Jer Kao. 2019-12-19. Optimal Real-Space Renormalization-Group Transformations with Artificial Neural Networks. https://arxiv.org/abs/1912.09005

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