Tipping Point Detection Using Reservoir Computing

作     者：Xin Li Qunxi Zhu Chengli Zhao Xuzhe Qian Xue Zhang Xiaojun Duan Wei Lin Xin Li;Qunxi Zhu;Chengli Zhao;Xuzhe Qian;Xue Zhang;Xiaojun Duan;Wei Lin

作者机构：College of ScienceNational University of Defense TechnologyChangshaHunan 410073China Research Institute of Intelligent Complex Systems and MOE Frontiers Center for Brain ScienceFudan UniversityShanghai 200433China Shanghai Artificial Intelligence LaboratoryShanghai 200232China School of Mathematical SciencesSCMSSCAMand CCSBFudan UniversityShanghai 200433China 

出 版 物：《Research》 (研究（英文）)

年 卷 期：2024年第2023卷第1期

页      面：779-790页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：the China Postdoctoral Science Foundation(no.2022M720817) by the Shanghai Postdoctoral Excellence Program(no.2021091) by the STCSM(nos.21511100200,22ZR1407300,and 23YF1402500) W.L.is supported by the National Natural Science Foundation of China(no.11925103) by the STCSM(nos.22JC1402500,22JC1401402,and 2021SHZDZX0103) 

主　　题：computing record ping 

摘      要：Detection in high fidelity of tipping points,the emergence of which is often induced by invisible changes in internal structures or/and external interferences,is paramountly beneficial to understanding and predicting complex dynamical systems(CDSs).Detection approaches,which have been fruitfully developed from several perspectives(e.g.,statistics,dynamics,and machine learning),have their own advantages but still encounter difficulties in the face of high-dimensional,fluctuating ***,using the reservoir computing(RC),a recently notable,resource-conserving machine learning method for reconstructing and predicting CDSs,we articulate a model-free framework to accomplish the detection only using the time series observationally recorded from the underlying unknown CDSs.