no code implementations • 20 May 2023 • Yao Du, Qing Li, Huawei Fan, Meng Zhan, Jinghua Xiao, Xingang Wang
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.
no code implementations • 26 Jan 2023 • Ya Wang, Liang Wang, Huawei Fan, Jun Ma, Hui Cao, Xingang Wang
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.
no code implementations • 23 Jul 2021 • Liang Wang, Huawei Fan, Jinghua Xiao, Yueheng Lan, Xingang Wang
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.
no code implementations • 24 Apr 2021 • Han Zhang, Huawei Fan, Liang Wang, Xingang Wang
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.
no code implementations • 13 Mar 2021 • Huawei Fan, Ling-Wei Kong, Ying-Cheng Lai, Xingang Wang
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.
no code implementations • 20 Nov 2020 • Huawei Fan, Ling-Wei Kong, Xingang Wang, Alan Hastings, Ying-Cheng Lai
Transients are fundamental to ecological systems with significant implications to management, conservation, and biological control.
no code implementations • 15 Nov 2020 • Yali Guo, Han Zhang, Liang Wang, Huawei Fan, Xingang Wang
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.
no code implementations • 6 Mar 2020 • Huawei Fan, Junjie Jiang, Chun Zhang, Xingang Wang, Ying-Cheng Lai
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.