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arXiv 1608.04416nlin.CD
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非线性动力系统的可视化分析:混沌、分形、自相似性和预测极限

Visual Analysis of Nonlinear Dynamical Systems: Chaos, Fractals, Self-Similarity and the Limits of Prediction

Geoff Boeing

摘要:

几乎所有的非平凡现实世界系统都是非线性动力系统。混沌描述了某些对初始条件有非常敏感依赖性的非线性动力系统。混沌系统总是确定性的,可能非常简单,但它们产生完全不可预测和发散的行为。非线性方程组很难解析求解,科学家们严重依赖视觉和定性方法来发现和分析非线性动力学。事实上,很少有领域在其开创性的创新中大量借鉴可视化方法:从奇异吸引子到分叉图,到蛛网图,再到相图和嵌入。尽管社会科学越来越多地研究这些类型的系统,但开创性的概念仍然模糊不清或被松散地采用。本文有三个目的。首先,它主张几种可视化方法来批判性地分析和理解非线性动力系统的行为。其次,它使用这些可视化来介绍非线性动力学,混沌,分形,自相似性和预测的限制的基础。最后,它介绍了Pynamical,一个开源的Python包,可以轻松地可视化和探索非线性动力系统的行为。

英文摘要:

Nearly all nontrivial real-world systems are nonlinear dynamical systems. Chaos describes certain nonlinear dynamical systems that have a very sensitive dependence on initial conditions. Chaotic systems are always deterministic and may be very simple, yet they produce completely unpredictable and divergent behavior. Systems of nonlinear equations are difficult to solve analytically, and scientists have relied heavily on visual and qualitative approaches to discover and analyze the dynamics of nonlinearity. Indeed, few fields have drawn as heavily from visualization methods for their seminal innovations: from strange attractors, to bifurcation diagrams, to cobweb plots, to phase diagrams and embedding. Although the social sciences are increasingly studying these types of systems, seminal concepts remain murky or loosely adopted. This article has three aims. First, it argues for several visualization methods to critically analyze and understand the behavior of nonlinear dynamical systems. Second, it uses these visualizations to introduce the foundations of nonlinear dynamics, chaos, fractals, self-similarity and the limits of prediction. Finally, it presents Pynamical, an open-source Python package to easily visualize and explore nonlinear dynamical systems' behavior.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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