On the risks of using double precision in numerical simulations of spatio-temporal chaos

TL;DR

This paper introduces a new high-precision numerical strategy called Clean Numerical Simulation (CNS) for accurately simulating spatio-temporal chaos over long durations, highlighting the risks of using standard double precision methods.

Contribution

The paper presents CNS, a more efficient high-order, multi-precision approach that reduces numerical errors, enabling reliable long-term chaos simulations and exposing limitations of double precision methods.

Findings

Double precision can cause significant errors in long-term chaos simulations.

CNS achieves reliable long-duration simulations of the complex Ginzburg-Landau equation.

Standard double precision may lead to qualitative and quantitative inaccuracies in chaos prediction.

Abstract

Due to the butterfly-effect, computer-generated chaotic simulations often deviate exponentially from the true solution, so that it is very hard to obtain a reliable simulation of chaos in a long-duration time. In this paper, a new strategy of the so-called Clean Numerical Simulation (CNS) in physical space is proposed for spatio-temporal chaos, which is computationally much more efficient than its predecessor (in spectral space). The strategy of the CNS is to reduce both of the truncation and round-off errors to a specified level by implementing high-order algorithms in multiple-precision arithmetic (with sufficient significant digits for all variables and parameters) so as to guarantee that numerical noise is below such a critical level in a temporal interval $t\in[0,T_c]$ that corresponding numerical simulation remains reliable over the whole interval. Without loss of generality, the complex Ginzburg-Landau equation (CGLE) is used to illustrate its validity. As a result, a reliable long-duration numerical simulation of the CGLE is achieved in the whole spatial domain over a long interval of time $t\in[0,3000]$, which is used as a reliable benchmark solution to investigate the influence of numerical noise by comparing it with the corresponding ones given by the 4th-order Runge-Kutta method in double precision (RKwD). Our results demonstrate that the use of double precision in simulations of chaos might lead to huge errors in the prediction of spatio-temporal trajectories and in statistics, not only quantitatively but also qualitatively, particularly in a long interval of time.