A Self-Adaptive Algorithm of the Clean Numerical Simulation (CNS) for Chaos

TL;DR

This paper enhances the Clean Numerical Simulation (CNS) method for chaotic systems by proposing strategies to improve computational efficiency and maintain accuracy, enabling long-term precise simulations despite exponential error growth.

Contribution

It introduces new strategies to significantly increase CNS efficiency and balance errors, extending the method's applicability to long-term chaotic system simulations.

Findings

Strategies effectively improve CNS computational efficiency.

Balanced error management enhances long-term simulation accuracy.

Examples demonstrate the validity of the proposed strategies.

Abstract

The background numerical noise $\varepsilon_{0} $ is determined by the maximum of truncation error and round-off error. For a chaotic system, the numerical error $\varepsilon(t)$ grows exponentially, say, $\varepsilon(t) = \varepsilon_{0} \exp(\kappa\,t)$, where $\kappa>0$ is the so-called noise-growing exponent. This is the reason why one can not gain a convergent simulation of chaotic systems in a long enough interval of time by means of traditional algorithms in double precision, since the background numerical noise $\varepsilon_{0}$ might stop decreasing because of the use of double precision. This restriction can be overcome by means of the clean numerical simulation (CNS), which can decrease the background numerical noise $\varepsilon_{0}$ to any required tiny level. A lot of successful applications show the novelty and validity of the CNS. In this paper, we further propose some strategies to greatly increase the computational efficiency of the CNS algorithms for chaotic dynamical systems. It is highly suggested to keep a balance between truncation error and round-off error and besides to progressively enlarge the background numerical noise $\varepsilon_{0}$, since the exponentially increasing numerical noise $\varepsilon(t)$ is much larger than it. Some examples are given to illustrate the validity of our strategies for the CNS.