arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1908.09683nlin.CDastro-ph.EPastro-ph.IM
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初始数据网格上庞加莱递归的大量计算和分析:一种映射混沌扩散的工具

Massive evaluation and analysis of Poincaré recurrences on grids of initial data: a tool to map chaotic diffusion

Ivan I. Shevchenko, Guillaume Rollin, Alexander V. Melnikov, José Lages

摘要:

我们提出了一种新的数值方法,旨在表征全球的行为,特别是混沌扩散,在动力系统。它是基于庞加莱递归统计的初始数据或参数值的大规模网格的分析。我们专注于哈密顿系统,分别为有界和无界相空间的情况下,具有该方法。在每种情况下的方法的实施例是特定的。我们比较了所提出的庞加莱递归方法(PRM)和自定义李雅普诺夫指数(LE)方法的性能,并表明它们几乎相同地揭示了全局动态。然而,新方法的一个主要优点,在已知的全球数值工具,如LE,FLI,MEGNO,和FA,是它允许一个构建,在一些近似,图表的局部扩散时间尺度。此外,它在算法上是简单和直接的应用。

英文摘要:

We present a novel numerical method aimed to characterize global behaviour, in particular chaotic diffusion, in dynamical systems. It is based on an analysis of the Poincaré recurrence statistics on massive grids of initial data or values of parameters. We concentrate on Hamiltonian systems, featuring the method separately for the cases of bounded and non-bounded phase spaces. The embodiments of the method in each of the cases are specific. We compare the performances of the proposed Poincaré recurrence method (PRM) and the custom Lyapunov exponent (LE) methods and show that they expose the global dynamics almost identically. However, a major advantage of the new method over the known global numerical tools, such as LE, FLI, MEGNO, and FA, is that it allows one to construct, in some approximation, charts of local diffusion timescales. Moreover, it is algorithmically simple and straightforward to apply.

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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