一个由ode控制的混沌系统Clean数值模拟的自动并行程序

IF 3.4 2区物理与天体物理 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer Physics Communications Pub Date : 2025-09-16 DOI:10.1016/j.cpc.2025.109855

Bo Zhang , Shijun Liao

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引用次数: 0

摘要

由于蝴蝶效应，混沌系统中的数值噪声呈指数增长，这是一个重大的挑战。这个问题可以通过使用廖在2009年提出的清洁数值模拟（CNS）来缓解，该方法可以在足够长的统计时间间隔内有效地将数值噪声降低到所需的（例如任意低的）水平。在本文中，我们提出了CNSPy，一种新颖，高效，自适应的CNS实现，用于获得由一组常微分方程（ode）控制的混沌系统的收敛（即可复制）数值模拟。该软件通过自动将python定义的ode转换为并行的C代码来自动化CNS计算工作流，从而消除了容易出错的手动派生和编码的需要，同时确保了高性能计算（HPC）环境中的高效率。代码是免费的，可以在https://github.com/sjtu-liao/cnspy上获得。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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An automated parallel program of Clean Numerical Simulation for chaotic systems governed by ODEs

Due to the butterfly-effect, numerical noise in chaotic systems grows exponentially, presenting a significant challenge. This issue can be mitigated through the use of Clean Numerical Simulation (CNS) proposed by Liao in 2009, which can effectively reduce numerical noise to a desired (say, arbitrarily low) level in an interval of time that is long enough for statistics. In this paper, we propose the CNSPy, a novel, highly efficient, self-adaptive CNS implementation to obtain the convergent (i.e. reproducible) numerical simulation of chaotic systems governed by a set of ordinary differential equations (ODEs). This software automates the CNS computational workflow by automatically converting Python-defined ODEs into a parallelized C code, eliminating the need for error-prone manual derivation and coding while ensuring high efficiency in high-performance computing (HPC) environments. The code is free and available at https://github.com/sjtu-liao/cnspy.

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来源期刊

Computer Physics Communications 物理-计算机：跨学科应用

CiteScore

12.10

自引率

3.20%

发文量

287

审稿时长

5.3 months

期刊介绍： The focus of CPC is on contemporary computational methods and techniques and their implementation, the effectiveness of which will normally be evidenced by the author(s) within the context of a substantive problem in physics. Within this setting CPC publishes two types of paper. Computer Programs in Physics (CPiP) These papers describe significant computer programs to be archived in the CPC Program Library which is held in the Mendeley Data repository. The submitted software must be covered by an approved open source licence. Papers and associated computer programs that address a problem of contemporary interest in physics that cannot be solved by current software are particularly encouraged. Computational Physics Papers (CP) These are research papers in, but are not limited to, the following themes across computational physics and related disciplines. mathematical and numerical methods and algorithms; computational models including those associated with the design, control and analysis of experiments; and algebraic computation. Each will normally include software implementation and performance details. The software implementation should, ideally, be available via GitHub, Zenodo or an institutional repository.In addition, research papers on the impact of advanced computer architecture and special purpose computers on computing in the physical sciences and software topics related to, and of importance in, the physical sciences may be considered.