Model-free forecasting of partially observable spatiotemporally chaotic systems.

Gupta, Vikrant; Li, Larry K B; Chen, Shiyi; Wan, Minping

Gupta, Vikrant; Li, Larry K B; Chen, Shiyi; Wan, Minping.

Afiliación

Gupta V; Guangdong Provincial Key Laboratory of Turbulence Research and Applications, Department of Mechanics and Aerospace Engineering, Southern University of Science and Technology, Shenzhen, 518055, PR China; Guangdong-Hong Kong-Macao Joint Laboratory for Data-Driven Fluid Mechanics and Engineering Applic
Li LKB; Department of Mechanical and Aerospace Engineering, Hong Kong University of Science and Technology, Hong Kong, China; Guangdong-Hong Kong-Macao Joint Laboratory for Data-Driven Fluid Mechanics and Engineering Applications, Hong Kong University of Science and Technology, Hong Kong, China.
Chen S; Guangdong Provincial Key Laboratory of Turbulence Research and Applications, Department of Mechanics and Aerospace Engineering, Southern University of Science and Technology, Shenzhen, 518055, PR China; Guangdong-Hong Kong-Macao Joint Laboratory for Data-Driven Fluid Mechanics and Engineering Applic
Wan M; Guangdong Provincial Key Laboratory of Turbulence Research and Applications, Department of Mechanics and Aerospace Engineering, Southern University of Science and Technology, Shenzhen, 518055, PR China; Guangdong-Hong Kong-Macao Joint Laboratory for Data-Driven Fluid Mechanics and Engineering Applic

Neural Netw ; 160: 297-305, 2023 Mar.

Article en En | MEDLINE | ID: mdl-36716509

Palabras clave

Chaos; Echo-state networks; Machine learning; Reservoir computing; Time-series forecasting; Turbulence

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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: Neural Netw Asunto de la revista: NEUROLOGIA Año: 2023 Tipo del documento: Article

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