Periodic Shadowing Sensitivity Analysis of Chaotic Systems
Davide Lasagna, Ati Sharma, Johan Meyers

TL;DR
This paper introduces a new periodic shadowing method for sensitivity analysis in chaotic systems, offering a simpler alternative to existing algorithms with comparable convergence properties, demonstrated on Lorenz equations.
Contribution
The paper proposes a novel periodic shadowing algorithm for chaotic systems, extending shadowing sensitivity analysis to periodic boundary conditions and demonstrating its effectiveness.
Findings
Periodic shadowing converges at similar rates as LSS.
Sensitivities converge to those from unstable periodic orbits.
Non-hyperbolicity introduces small bias in finite differences.
Abstract
The sensitivity of long-time averages of a hyperbolic chaotic system to parameter perturbations can be determined using the shadowing direction, the uniformly-bounded-in-time solution of the sensitivity equations. Although its existence is formally guaranteed for certain systems, methods to determine it are hardly available. One practical approach is the Least-Squares Shadowing (LSS) algorithm (Q Wang, SIAM J Numer Anal 52, 156, 2014), whereby the shadowing direction is approximated by the solution of the sensitivity equations with the least square average norm. Here, we present an alternative, potentially simpler shadowing-based algorithm, termed periodic shadowing. The key idea is to obtain a bounded solution of the sensitivity equations by complementing it with periodic boundary conditions in time. We show that this is not only justifiable when the reference trajectory is itself…
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