Estimating covariant Lyapunov vectors from data

TL;DR

This paper introduces a new data-driven method for estimating covariant Lyapunov vectors directly from data records, applicable to high-dimensional systems, aiding in understanding system stability and predicting critical events.

Contribution

The novel approach allows estimation of covariant Lyapunov vectors from data without requiring system equations, even in high-dimensional settings up to 128 dimensions.

Findings

Accurately estimates covariant Lyapunov vectors from data.

Applicable to high-dimensional datasets up to 128 dimensions.

Enables new applications in data analysis and prediction.

Abstract

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.