Predicting amplitude death with machine learning

Rui Xiao, Ling Wei Kong, Zhong Kui Sun, Ying Cheng Lai

科研成果: 期刊稿件文章同行评审

38 引用 (Scopus)

摘要

In nonlinear dynamics, a parameter drift can lead to a sudden and complete cessation of the oscillations of the state variables - the phenomenon of amplitude death. The underlying bifurcation is one at which the system settles into a steady state from chaotic or regular oscillations. As the normal functioning of many physical, biological, and physiological systems hinges on oscillations, amplitude death is undesired. To predict amplitude death in advance of its occurrence based solely on oscillatory time series collected while the system still functions normally is a challenge problem. We exploit machine learning to meet this challenge. In particular, we develop the scheme of "parameter-aware"reservoir computing, where training is conducted for a small number of bifurcation parameter values in the oscillatory regime to enable prediction upon a parameter drift into the regime of amplitude death. We demonstrate successful prediction of amplitude death for three prototypical dynamical systems in which the transition to death is preceded by either chaotic or regular oscillations. Because of the completely data-driven nature of the prediction framework, potential applications to real-world systems can be anticipated.

源语言英语
文章编号014205
期刊Physical Review E - Statistical, Nonlinear, and Soft Matter Physics
104
1
DOI
出版状态已出版 - 7月 2021

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