渗透(认知心理学)
渗流阈值
渗流理论
节点(物理)
相变
计算机科学
连续介质渗流理论
统计物理学
星团(航天器)
人工神经网络
集团渗流法
算法
人工智能
数学
拓扑(电路)
渗流临界指数
物理
临界指数
聚类分析
组合数学
热力学
生物
电阻率和电导率
神经科学
量子力学
程序设计语言
作者
Su‐Bin Oh,Kwangjong Choi,B. Kahng
标识
DOI:10.1088/1742-5468/aceef1
摘要
Abstract Recently, a machine learning (ML) approach has been proposed to determine the percolation threshold and critical behaviors of percolation transitions (PTs), based on the ML algorithm used for the phase transition in thermal equilibrium systems. However, we have observed that the conventional ML approach used for thermal systems does not accurately provide the percolation threshold, in particular when the training regions for ML are asymmetrical with respect to its known value. Here, we remark that percolation is a geometric phase transition, and thus global information, rather than the local configurations used in thermal systems, is needed to determine the percolation threshold. To address this, we assign a parent node index to each node, which is updated during cluster merging, capturing global information on the ancestor of each node. Utilizing this quantity as input data for the convolutional neural network in the ML algorithm, we successfully obtain the correct percolation threshold regardless of whether the training regions are symmetric or asymmetric with respect to the known value. This validity holds independently of the PT type: continuous, hybrid, or discontinuous. As the concept of percolation is applied to various phenomena, this ML algorithm could be used ubiquitously.
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