Optimal Real-Space Renormalization-Group Transformations with Artificial Neural Networks
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 after the first step of the transformation.
Cite
@article{arxiv.1912.09005,
title = {Optimal Real-Space Renormalization-Group Transformations with Artificial Neural Networks},
author = {Jui-Hui Chung and Ying-Jer Kao},
journal= {arXiv preprint arXiv:1912.09005},
year = {2019}
}
Comments
Published as a proceeding of Workshop on Machine Learning and the Physical Sciences at the 33rd Conference on Neural Information Processing Systems (NeurIPS) https://ml4physicalsciences.github.io/