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Inferring Attracting Basins of Power System with Machine Learning
Power systems dominated by renewable energy encounter frequently large, random disturbances, and a critical challenge faced in power-system management is how to anticipate accurately whether the perturbed systems will return to the functional state after the transient or collapse.
Breathing cluster in complex neuron-astrocyte networks
It is revealed that the contents of the cluster are determined by the network symmetry and the breathing activities are due to the interplay between the neural network and the astrocyte.
Criticality in Reservoir Computer of Coupled Phase Oscillators
Additionally, it is found that despite the synchronization degree of the original network, once properly trained, the reservoir network is always developed to the same critical state, exemplifying the "attractor" nature of this state in machine learning.
Learning Hamiltonian dynamics by reservoir computer
Reconstructing the KAM dynamics diagram of Hamiltonian system from the time series of a limited number of parameters is an outstanding question in nonlinear science, especially when the Hamiltonian governing the system dynamics are unknown.
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.
Synchronization within synchronization: transients and intermittency in ecological networks
Transients are fundamental to ecological systems with significant implications to management, conservation, and biological control.
Transfer learning of chaotic systems
Here we investigate transfer learning of chaotic systems from the perspective of synchronization-based state inference, in which a reservoir computer trained by chaotic system A is used to infer the unmeasured variables of chaotic system B, while A is different from B in either parameter or dynamics.
Long-term prediction of chaotic systems with recurrent neural networks
Reservoir computing systems, a class of recurrent neural networks, have recently been exploited for model-free, data-based prediction of the state evolution of a variety of chaotic dynamical systems.
