用神经网络自动识别1+1维定向渗流中的相变与隐藏模式
Identifying internal patterns in (1+1)-dimensional directed percolation using neural networks
- 融合CNN、TCN和GRU的深度模型，直接处理原始数据
- 成功复现相图并准确为配置打相位标签
- 适合研究复杂系统中隐含结构的物理与计算学者
本文提出一种基于神经网络的方法，用于自动检测(1+1)-维复制过程中的相变及分类隐藏的渗流模式。所提出的网络模型结合了卷积神经网络（CNN）、时序卷积网络（TCN）和门控循环单元（GRU），直接在原始配置上训练，无需人工特征提取。该模型能够重现相图，并对配置进行相位标注。结果表明，深层架构可从数值实验的原始数据中提取层次化结构。
原文摘要 · Abstract (English)
In this paper we present a neural network-based method for the automatic detection of phase transitions and classification of hidden percolation patterns in a (1+1)-dimensional replication process. The proposed network model is based on the combination of CNN, TCN and GRU networks, which are trained directly on raw configurations without any manual feature extraction. The network reproduces the phase diagram and assigns phase labels to configurations. It shows that deep architectures are capable of extracting hierarchical structures from the raw data of numerical experiments.
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