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arXiv 2407.04397nlin.CD
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通过多粒子方法计算广义对齐指数(GALI)高效检测混沌

Efficient detection of chaos through the computation of the Generalized Alignment Index (GALI) by the multi-particle method

University of Cape Town(开普敦大学);Le Mans Université(勒芒大学);CNRS(法国国家科学研究中心);Max Planck Institute for the Physics of Complex Systems(马克斯·普朗克复杂系统物理研究所)

Bertin Many Manda, Malcolm Hillebrand, Charalampos Skokos

AI总结:

提出一种多粒子方法计算GALI混沌指标,避免变分方程,通过误差估计和数值模拟验证其鲁棒性和准确性,适用于高维哈密顿系统。

摘要:

我们提出了一种使用多粒子方法计算广义对齐指数(GALI)的方法,这是一种快速有效的混沌指标,避免了变分方程。通过推导变分方法(VM)和多粒子方法(MPM)的主导阶误差估计,并在两个典型模型上进行了广泛的数值模拟验证了该方法的鲁棒性和准确性:两个自由度的Hénon-Heiles系统和多维$β$-Fermi-Pasta-Ulam-Tsingou振子链。详细研究了GALI精度对控制参数(如重整化时间、积分时间步长和偏差向量大小)的依赖性。我们测试了以双精度精度($\varepsilon \approx 10^{-16}$)实现的MPM,发现对于偏差向量大小$d_0\approx \varepsilon^{1/2}$、重整化时间$τ\lesssim 1$和相对能量误差$E_r \lesssim \varepsilon^{1/2}$,其性能可靠。这些结果适用于具有多个自由度的系统,并表明MPM是研究哈密顿系统混沌动力学的鲁棒且高效的方法。我们的工作通过消除对变分方程的需求,使得在大量系统中使用GALI探索混沌动力学成为可能。

英文摘要:

We present a method for the computation of the Generalized Alignment Index (GALI), a fast and effective chaos indicator, using a multi-particle approach that avoids variational equations. We show that this approach is robust and accurate by deriving a leading-order error estimation for both the variational (VM) and the multi-particle (MPM) methods, which we validate by performing extensive numerical simulations on two prototypical models: the two degrees of freedom Hénon-Heiles system and the multidimensional $β$-Fermi-Pasta-Ulam-Tsingou chain of oscillators. The dependence of the accuracy of the GALI on control parameters such as the renormalization time, the integration time step and the deviation vector size is studied in detail. We test the MPM implemented with double precision accuracy ($\varepsilon \approx 10^{-16}$) and find that it performs reliably for deviation vector sizes $d_0\approx \varepsilon^{1/2}$, renormalization times $τ\lesssim 1$, and relative energy errors $E_r \lesssim \varepsilon^{1/2}$. These results hold for systems with many degrees of freedom and demonstrate that the MPM is a robust and efficient method for studying the chaotic dynamics of Hamiltonian systems. Our work makes it possible to explore chaotic dynamics with the GALI in a vast number of systems by eliminating the need for variational equations.

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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