Computational assessment of smooth and rough parameter dependence of statistics in chaotic dynamical systems

TL;DR

This paper introduces a numerical method to evaluate the smoothness of statistical responses in chaotic systems by analyzing the Lebesgue-integrability of a density gradient, distinguishing between smooth and rough parameter dependencies.

Contribution

It develops a recursive formula for the density gradient and demonstrates a procedure to assess the differentiability of statistics in chaotic systems.

Findings

Existence of derivatives depends on Lebesgue-integrability of density gradient.

The method distinguishes smooth and rough regions in parameter space.

Numerical experiments validate the approach on low-dimensional systems.

Abstract

An assumption of smooth response to small parameter changes, of statistics or long-time averages of a chaotic system, is generally made in the field of sensitivity analysis, and the parametric derivatives of statistical quantities are critically used in science and engineering. In this paper, we propose a numerical procedure to assess the differentiability of statistics with respect to parameters in chaotic systems. We numerically show that the existence of the derivative depends on the Lebesgue-integrability of a certain density gradient function, which we define as the derivative of logarithmic SRB density along the unstable manifold. We develop a recursive formula for the density gradient that can be efficiently computed along trajectories, and demonstrate its use in determining the differentiability of statistics. Our numerical procedure is illustrated on low-dimensional chaotic systems whose statistics exhibit both smooth and rough regions in parameter space.