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On the behavior of the Generalized Alignment Index (GALI) method for dissipative systems
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The behavior of the Generalized Alignment Index (GALI) method has been extensively studied and successfully applied for the detection of chaotic motion in conservative Hamiltonian systems, yet its application to non-Hamiltonian dissipative systems remains relatively unexplored. In this work, we fill this gap by investigating the GALI's ability to identify stable fixed points, stable limit cycles, chaotic (strange) and hyperchaotic attractors in dissipative systems generated by both continuous and discrete time dynamics, and compare its performance to the analysis achieved by the computation of the spectrum of Lyapunov exponents. Through a comprehensive study of three classical dissipative models, namely the 3D Lorenz system, a modified Lorenz 4D hyperchaotic system, and the 3D generalized hyperchaotic H\'enon map, we examine GALI's behavior, and possible limitations, in detecting chaotic motion, as well as the presence of different types of attractors occurring in dissipative dynamical systems. We find that the GALI successfully detects chaotic motion, as well as stable fixed points, but it faces difficulties in distinctly discriminating between stable limit cycles, chaotic attractors, and hyperchaotic motion.
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Chaoticus: a parallel approach to the computation of chaos indicators
Chaoticus is a Python package that moves chaos indicator computations (SALI, GALI, Lagrangian descriptors, Lyapunov exponents) onto GPUs and claims order-of-magnitude speedups.
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