SIAM Journal on Applied Dynamical Systems, Volume 19, Issue 4, Page 2720-2736, January 2020.
In this paper we introduce a new approach to computing hidden features of sampled vector fields. The basic idea is to convert the vector field data into a graph structure and use tools designed for automatic, unsupervised analysis of graphs. Using a few data sets, we show that the collected features of the vector fields are correlated with the dynamics known for analytic models which generate the data. In particular the method may be useful in the analysis of data sets where the analytic model is poorly understood or not known.

中文翻译：

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用于样本矢量场的无监督特征学习

SIAM应用动力系统杂志，第19卷，第4期，第2720-2736页，2020
年1月。在本文中，我们介绍了一种计算采样矢量场的隐藏特征的新方法。基本思想是将矢量场数据转换为图形结构，并使用设计用于自动，无监督图形分析的工具。使用一些数据集，我们表明向量场的收集特征与生成数据的解析模型已知的动力学相关。特别地，该方法在分析模型了解不多或未知的数据集的分析中可能有用。