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arXiv 1701.08262nlin.CD
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收敛混沌

Convergent Chaos

Marc Pradas, Alain Pumir, Greg Huber, Michael Wilkinson

摘要:

混沌被广泛理解为对初始条件敏感依赖的结果。这是相空间不稳定性的结果,它以指数方式分离轨迹。在这里,我们证明,这一标准应加以完善。尽管其整体固有的不稳定性,轨迹可能是非常强烈的收敛,在相空间在极长的时间内,我们的调查一个简单的混沌系统(一个现实的模型,小机构在湍流)。我们确定,这种强收敛是一个多方面的现象,其中集群是激烈的,广泛的和平衡的空白的其他地区。幂律表示无标度特征,描述了系统中粒子的分布。我们使用大偏差和极值统计来解释这种效应。我们的研究结果表明,“蝴蝶效应”的解释需要仔细限定。我们认为,混合和聚类过程的结合,使我们的特定模型相关的理解简单的生物体的进化。最后,这个概念的“收敛混沌”,这意味着存在的条件下,不确定性是出乎意料的小,也可能是相关的保险和期货合约的估值。

英文摘要:

Chaos is widely understood as being a consequence of sensitive dependence upon initial conditions. This is the result of an instability in phase space, which separates trajectories exponentially. Here, we demonstrate that this criterion should be refined. Despite their overall intrinsic instability, trajectories may be very strongly convergent in phase space over extremely long periods, as revealed by our investigation of a simple chaotic system (a realistic model for small bodies in a turbulent flow). We establish that this strong convergence is a multi-facetted phenomenon, in which the clustering is intense, widespread and balanced by lacunarity of other regions. Power laws, indicative of scale-free features, characterise the distribution of particles in the system. We use large-deviation and extreme-value statistics to explain the effect. Our results show that the interpretation of the 'butterfly effect' needs to be carefully qualified. We argue that the combination of mixing and clustering processes makes our specific model relevant to understanding the evolution of simple organisms. Lastly, this notion of 'convergent chaos', which implies the existence of conditions for which uncertainties are unexpectedly small, may also be relevant to the valuation of insurance and futures contracts.

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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