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.
翻译:在气候、物理、材料科学、流行病学、金融等等方面,相形照照照是一个重要的主题。对机器学习方法的穿透阈值的预测仍然具有挑战性。在本文中,我们建立了一个强大的图形进化神经网络,以以监督和不受监督的方式研究纵深。从监督的学习角度看,图形进化神经网络同时并正确地培训不同层型的数据,如方形和三角层。对于未受监督的视角而言,将图形进化神经网络和混乱方法结合起来,“W”塑造的性能可以获得渗透阈值。这项工作的发现为建立一个更普遍的框架以探究与渗透相关的现象开辟了可能性。