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Found 5 papers, 4 papers with code
Predicting discrete-time bifurcations with deep learning
Here, we train a deep learning classifier to provide an EWS for the five local discrete-time bifurcations of codimension-1.
Universal Early Warning Signals of Phase Transitions in Climate Systems
The potential for complex systems to exhibit tipping points in which an equilibrium state undergoes a sudden and often irreversible shift is well established, but prediction of these events using standard forecast modeling techniques is quite difficult.
Stochastically forced ensemble dynamic mode decomposition for forecasting and analysis of near-periodic systems
Time series forecasting remains a central challenge problem in almost all scientific disciplines.
Sparse Identification of Slow Timescale Dynamics
We show that for sufficiently disparate timescales this discovered mapping can be used to discover the continuous-time slow dynamics, thus providing a novel tool for extracting dynamics on multiple timescales.
Principal component trajectories for modeling spectrally-continuous dynamics as forced linear systems
Delay embeddings of time series data have emerged as a promising coordinate basis for data-driven estimation of the Koopman operator, which seeks a linear representation for observed nonlinear dynamics.
Computational Physics Systems and Control Systems and Control
