Searching chaotic saddles in high dimensions

We propose new methods to numerically approximate non-attracting sets governing transiently-chaotic systems. Trajectories starting in a vicinity $Ω$ of these sets escape $Ω$ in a finite time $τ$ and the problem is to find initial conditions ${\bf x} \in Ω$ with increasingly large $τ= τ({\bf x})$. We search points ${\bf x}'$ with $τ({\bf x}')>τ({\bf x})$ in a {\it search domain} in $Ω$. Our first method considers a search domain with size that decreases exponentially in $τ$, with an exponent proportional to the largest Lyapunov exponent $λ_1$. Our second method considers anisotropic search domains in the {\it tangent} unstable manifold, where each direction scale as the inverse of the corresponding {\it expanding} singular value of the Jacobian matrix of the iterated map. We show that both methods outperform the state-of-the-art {\it Stagger-and-Step} method (Sweet, Nusse, and York, Phys. Rev. Lett. {\bf 86}, 2261, 2001) but that only the anisotropic method achieves an efficiency independent of $τ$ for the case of high-dimensional systems with multiple positive Lyapunov exponents. We perform simulations in a chain of coupled Hénon maps in up to 24 dimensions ($12$ positive Lyapunov exponents). This suggests the possibility of characterizing also non-attracting sets in spatio-temporal systems.

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