Search Results for author:
Found 7 papers, 1 papers with code
Machine-learning prediction of tipping and collapse of the Atlantic Meridional Overturning Circulation
Recent research on the Atlantic Meridional Overturning Circulation (AMOC) raised concern about its potential collapse through a tipping point due to the climate-change caused increase in the freshwater input into the North Atlantic.
Model-free tracking control of complex dynamical trajectories with machine learning
We develop a model-free, machine-learning framework to control a two-arm robotic manipulator using only partially observed states, where the controller is realized by reservoir computing.
Emergence of a stochastic resonance in machine learning
Can noise be beneficial to machine-learning prediction of chaotic systems?
Digital twins of nonlinear dynamical systems
We articulate the design imperatives for machine-learning based digital twins for nonlinear dynamical systems subject to external driving, which can be used to monitor the ``health'' of the target system and anticipate its future collapse.
Anticipating synchronization with machine learning
In applications of dynamical systems, situations can arise where it is desired to predict the onset of synchronization as it can lead to characteristic and significant changes in the system performance and behaviors, for better or worse.
Machine learning prediction of critical transition and system collapse
Remarkably, for a parameter drift through the critical point, the machine with the input parameter channel is able to predict not only that the system will be in a transient state, but also the average transient time before the final collapse.
Synchronization within synchronization: transients and intermittency in ecological networks
Transients are fundamental to ecological systems with significant implications to management, conservation, and biological control.
