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

Bibliographic Details
Title: Efficient detection of chaos through the computation of the Generalized Alignment Index (GALI) by the multi-particle method
Authors: Manda, Bertin Many, Hillebrand, Malcolm, Skokos, Charalampos
Publication Year: 2024
Collection: Nonlinear Sciences
Subject Terms: Nonlinear Sciences - Chaotic Dynamics
More Details: 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 H\'enon-Heiles system and the multidimensional $\beta$-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 $\tau \lesssim 1$, 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.
Comment: 17 pages, 13 figures, Accepted for publication in Communications in Nonlinear Science and Numerical Simulation
Document Type: Working Paper
Access URL: http://arxiv.org/abs/2407.04397
Accession Number: edsarx.2407.04397
Database: arXiv
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