Extrapolating tipping points and simulating non-stationary dynamics of complex systems using efficient machine learning
Machine Learning
2023-12-12 v1 Neural and Evolutionary Computing
Chaotic Dynamics
Computational Physics
Abstract
Model-free and data-driven prediction of tipping point transitions in nonlinear dynamical systems is a challenging and outstanding task in complex systems science. We propose a novel, fully data-driven machine learning algorithm based on next-generation reservoir computing to extrapolate the bifurcation behavior of nonlinear dynamical systems using stationary training data samples. We show that this method can extrapolate tipping point transitions. Furthermore, it is demonstrated that the trained next-generation reservoir computing architecture can be used to predict non-stationary dynamics with time-varying bifurcation parameters. In doing so, post-tipping point dynamics of unseen parameter regions can be simulated.
Cite
@article{arxiv.2312.06283,
title = {Extrapolating tipping points and simulating non-stationary dynamics of complex systems using efficient machine learning},
author = {Daniel Köglmayr and Christoph Räth},
journal= {arXiv preprint arXiv:2312.06283},
year = {2023}
}