Shadowing Lemma and Chaotic Orbit Determination
Federica Spoto, Andrea Milani

TL;DR
This paper investigates the limits of orbit determination for chaotic systems, demonstrating how uncertainties evolve with observations and highlighting the impact of chaos on the predictability horizon.
Contribution
It introduces a method to compute shadowing orbits in chaotic systems and analyzes the behavior of uncertainties in orbit determination beyond the predictability horizon.
Findings
Uncertainty decreases exponentially for non-chaotic orbits with more observations.
For chaotic orbits, the computability horizon limits orbit determination accuracy.
Adding parameters increases uncertainty, which decreases slowly with more data.
Abstract
Orbit determination is possible for a chaotic orbit of a dynamical system, given a finite set of observations, provided the initial conditions are at the central time. In a simple discrete model, the standard map, we tackle the problem of chaotic orbit determination when observations extend beyond the predictability horizon. If the orbit is hyperbolic, a shadowing orbit is computed by the least squares orbit determination. We test both the convergence of the orbit determination iterative procedure and the behaviour of the uncertainties as a function of the maximum number of map iterations observed. When the initial conditions belong to a chaotic orbit, the orbit determination is made impossible by numerical instability beyond a computability horizon, which can be approximately predicted by a simple formula. Moreover, the uncertainty of the results is sharply increased if a dynamical…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
