从数据中估计协变李雅普诺夫向量
Estimating covariant Lyapunov vectors from data
摘要：
协变李雅普诺夫向量表征了动力系统中扰动增长的方向。它们也被作为关键转变和极端事件的预测因子进行研究。对于许多应用，例如预测，有必要从数据中估计向量，因为模型方程对于许多有趣的现象是未知的。我们提出了一种新的方法估计协变李雅普诺夫向量的基础上的数据记录，而不知道系统的基本方程。与以前的方法相比，我们的方法可以应用于高维数据集。我们证明，这种纯粹的数据驱动的方法可以准确地估计协变Lyapunpov向量从低维和高维动力系统产生的数据记录。协变李雅普诺夫向量估计在此贡献的时间序列的最高维度是128。能够从数据记录中推断协变李雅普诺夫向量可以鼓励未来在数据分析和基于数据的预测中的许多应用。
英文摘要：
Covariant Lyapunov vectors characterize the directions along which perturbations in dynamical systems grow. They have also been studied as predictors of critical transitions and extreme events. For many applications like, for example, prediction, it is necessary to estimate the vectors from data since model equations are unknown for many interesting phenomena. We propose a novel method for estimating covariant Lyapunov vectors based on data records without knowing the underlying equations of the system. In contrast to previous approaches, our approach can be applied to high-dimensional data-sets. We demonstrate that this purely data-driven approach can accurately estimate covariant Lyapunpov vectors from data records generated by low and high-dimensional dynamical systems. The highest dimension of a time-series from which covariant Lyapunov vectors were estimated in this contribution is 128. Being able to infer covariant Lyapunov vectors from data-records could encourage numerous future applications in data-analysis and data-based predictions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
