Stability analysis of chaotic systems from data

TL;DR

This paper introduces a data-driven method using Echo State Networks to accurately infer the Jacobian and stability properties of chaotic systems from observational data, bypassing the need for explicit equations.

Contribution

It proposes a novel approach combining ESNs with stability analysis to learn the Jacobian and stability metrics directly from data, enabling analysis of nonlinear chaotic systems without explicit models.

Findings

Accurately infers the Jacobian of chaotic systems from data.

Replicates stability metrics such as Lyapunov exponents and vectors.

Demonstrates negligible numerical errors in stability inference.

Abstract

The prediction of the temporal dynamics of chaotic systems is challenging because infinitesimal perturbations grow exponentially. The analysis of the dynamics of infinitesimal perturbations is the subject of stability analysis. In stability analysis, we linearize the equations of the dynamical system around a reference point, and compute the properties of the tangent space (i.e., the Jacobian). The main goal of this paper is to propose a method that learns the Jacobian, thus, the stability properties, from observables (data). First, we propose the Echo State Network (ESN) with the Recycle Validation as a tool to accurately infer the chaotic dynamics from data. Second, we mathematically derive the Jacobian of the Echo State Network, which provides the evolution of infinitesimal perturbations. Third, we analyse the stability properties of the Jacobian inferred from the ESN, and compare them with the benchmark results obtained by linearizing the equations. The ESN correctly infers the nonlinear solution, and its tangent space with negligible numerical errors. In detail, (i) the long-term statistics of the chaotic state; (ii) the covariant Lyapunov vectors; (ii) the Lyapunov spectrum; (iii) the finite-time Lyapunov exponents; (iv) and the angles between the stable, neutral, and unstable splittings of the tangent space (the degree of hyperbolicity of the attractor). This work opens up new opportunities for the computation of stability properties of nonlinear systems from data, instead of equations.