使用图卷积神经网络学习渗流模型
Machine learning of percolation models using graph convolutional neural networks
AI总结：
本文构建图卷积神经网络，以监督和无监督方式研究渗流，能同时训练不同晶格数据，并结合混淆方法通过“W”形曲线获得渗流阈值，为构建更通用的渗流探索框架奠定基础。
摘要：
渗流是气候、物理、材料科学、流行病学和金融等领域的重要课题。用机器学习方法预测渗流阈值仍具挑战。本文构建了强大的图卷积神经网络，以监督和无监督方式研究渗流。从监督学习角度，图卷积神经网络同时正确训练不同晶格类型的数据，如正方晶格和三角晶格。从无监督角度，结合图卷积神经网络与混淆方法，可通过“W”形性能曲线获得渗流阈值。本研究的发现为构建能探索渗流相关现象的更通用框架开辟了可能。
英文摘要：
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.
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