This study investigates the use of covariant Lyapunov vectors and their respective angles for detecting transitions between metastable states in dynamical systems, as recently discussed in several atmospheric sciences applications. In a first step, the needed underlying dynamical models are derived from data using a non-parametric model-based clustering framework. The covariant Lyapunov vectors are then approximated based on these data-driven models. The data-based numerical approach is tested using three well-understood example systems with increasing dynamical complexity, identifying properties that allow for a successful application of the method: in particular, the method is identified to require a clear multiple time scale structure with fast transitions between slow subsystems. The latter slow dynamics should be dynamically characterized by invariant neutral directions of the linear approximation model.
Guidelines for data-driven approaches to study transitions in multiscale systems: The case of Lyapunov vectors
Note: This article is part of the Focus Issue, Theory-informed and Data-driven Approaches to Advance Climate Sciences.
Akim Viennet, Nikki Vercauteren, Maximilian Engel, Davide Faranda; Guidelines for data-driven approaches to study transitions in multiscale systems: The case of Lyapunov vectors. Chaos 1 November 2022; 32 (11): 113145. https://doi.org/10.1063/5.0093804
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