Data-driven reconstruction of chaotic dynamical equations: the H\'enon-Heiles type system

TL;DR

This paper investigates a family of two-dimensional potentials exhibiting integrable and chaotic behaviors, characterizes their dynamics, and evaluates the data-driven SINDy method's effectiveness in reconstructing their governing equations, especially near chaos transitions.

Contribution

It introduces a chaotic system based on the Henon-Heiles model as a benchmark for testing the SINDy algorithm's accuracy in reconstructing dynamical equations from data.

Findings

SINDy accurately reconstructs equations in regular regimes.

The method remains robust near chaos transitions.

SINDy can generate approximate analytical solutions for periodic trajectories.

Abstract

In this study, the classical two-dimensional potential $V_N=\frac{1}{2}\,m\,\omega^2\,r^2 + \frac{1}{N}\,r^N\,\sin(N\,\theta)$, $N \in {\mathbb Z}^+$, is considered. At $N=1,2$, the system is superintegrable and integrable, respectively, whereas for $N>2$ it exhibits a richer chaotic dynamics. For instance, at $N=3$ it coincides with the H\'enon-Heiles system. The periodic, quasi-periodic and chaotic motions are systematically characterized employing time series, Poincar\'e sections, symmetry lines and the largest Lyapunov exponent as a function of the energy $E$ and the parameter $N$. Concrete results for the lowest cases $N=3,4$ are presented in complete detail. This model is used as a benchmark system to estimate the accuracy of the Sparse Identification of Nonlinear Dynamical Systems (SINDy) method, a data-driven algorithm which reconstructs the underlying governing dynamical equations. We pay special attention at the transition from regular motion to chaos and how this influences the precision of the algorithm. In particular, it is shown that SINDy is a robust and stable tool possessing the ability to generate non-trivial approximate analytical expressions for periodic trajectories as well.