Monte Carlo Basin Bifurcation Analysis

Abstract

Many high-dimensional complex systems exhibit an enormously complex landscape of possible asymptotic states. Here, we present a numerical approach geared towards analyzing such systems. It is situated between the classical analysis with macroscopic order parameters and a more thorough, detailed bifurcation analysis. With our machine learning method, based on random sampling and clustering methods, we are able to characterize the different asymptotic states or classes thereof and even their basins of attraction. In order to do this, suitable, easy to compute, statistics of trajectories with randomly generated initial conditions and parameters are clustered by an algorithm such as DBSCAN. Due to its modular and flexible nature, our method has a wide range of possible applications. Typical applications are oscillator networks, but it is not limited only to ordinary differential equation systems, every complex system yielding trajectories, such as maps or agent-based models, can be analyzed, as we show by applying it the Dodds-Watts model, a generalized SIRS-model. A second order Kuramoto model and a Stuart-Landau oscillator network, each exhibiting a complex multistable regime, are shown as well. The method is available to use as a package for the Julia language.

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Cited by in corpus (6)

• Predicting Basin Stability of Power Grids using Graph Neural Networks
• Framework for global stability analysis of dynamical systems
• Dynamical Mean-Field Theory of Complex Systems on Sparse Directed Networks
• NetworkDynamics.jl -- Composing and simulating complex networks in Julia
• Resonant Solitary States in Complex Networks
• Ambient Forcing: Sampling Local Perturbations in Constrained Phase Spaces