论文标题
使用图形卷积神经网络对渗透模型的机器学习
Machine learning of percolation models using graph convolutional neural networks
论文作者
论文摘要
渗透是气候，物理，材料科学，流行病学，金融等重要主题。用机器学习方法预测渗透阈值仍然具有挑战性。在本文中，我们建立了一个强大的图形卷积神经网络，以监督和无监督的方式研究渗透。从监督的学习角度，图形卷积神经网络同时训练了不同晶格类型的数据，例如正方形和三角形晶格。对于无监督的视角，将图形卷积神经网络和混乱方法结合在一起，可以通过“ W”形性能获得渗透阈值。这项工作的发现打开了建立一个更通用的框架的可能性，该框架可以探究与渗透相关的现象。
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.
