Machine learning of percolation models using graph convolutional neural networks
Abstract
Percolation is an important topic in climate, physics, materials science, epidemiology, finance, and so on. Prediction of percolation thresholds with machine learning methods remains challenging. In this paper, we build a powerful graph convolutional neural network to study the percolation in both supervised and unsupervised ways. From a supervised learning perspective, the graph convolutional neural network simultaneously and correctly trains data of different lattice types, such as the square and triangular lattices. For the unsupervised perspective, combining the graph convolutional neural network and the confusion method, the percolation threshold can be obtained by the "W" shaped performance. The finding of this work opens up the possibility of building a more general framework that can probe the percolation-related phenomenon.
Cite
@article{arxiv.2207.03368,
title = {Machine learning of percolation models using graph convolutional neural networks},
author = {Hua Tian and Lirong Zhang and Youjin Deng and Wanzhou Zhang},
journal= {arXiv preprint arXiv:2207.03368},
year = {2023}
}
Comments
Data from unsupervised machine learning method need to be re-examined