Efficient computation of linear response of chaotic attractors with one-dimensional unstable manifolds

TL;DR

This paper introduces the S3 algorithm for efficiently computing the linear response of chaotic attractors with one-dimensional unstable manifolds, enabling practical sensitivity analysis in chaotic systems.

Contribution

The paper develops a novel decomposition of Ruelle's formula and algorithms for stable and unstable contributions, facilitating efficient sensitivity computations in chaotic dynamics.

Findings

The S3 algorithm converges like a Monte Carlo approximation.

The stable contribution can be computed via a regularized tangent equation.

The unstable contribution is converted into an ergodic average for efficient computation.

Abstract

This paper presents the space-split sensitivity or the S3 algorithm to transform Ruelle's linear response formula into a well-conditioned ergodic-averaging computation. We prove a decomposition of Ruelle's formula that is differentiable on the unstable manifold, which we assume to be one-dimensional. This decomposition of Ruelle's formula ensures that one of the resulting terms, the stable contribution, can be computed using a regularized tangent equation, similar to in a non-chaotic system. The remaining term, known as the unstable contribution, is regularized and converted into an efficiently computable ergodic average. In this process, we develop new algorithms, which may be useful beyond linear response, to compute i) a fundamental statistical quantity we introduce called the density gradient, and ii) the unstable derivatives of the regularized tangent vector field and the unstable direction. We prove that the S3 algorithm, which combines these computational ingredients that enter the stable and unstable contribution, converges like a Monte Carlo approximation of Ruelle's formula. The algorithm presented here is hence a first step toward full-fledged applications of sensitivity analysis in chaotic systems, wherever such applications have been limited due to lack of availability of long-term sensitivities.