Model-free forecasting of partially observable spatiotemporally chaotic systems

Gupta，Vikrant1,2; Li，Larry K.B.3,4; Chen，Shiyi1,2,5; Wan，Minping1,2,6

2023-03-01

10.1016/j.neunet.2023.01.013

NEURAL NETWORKS   影响因子和分区

0893-6080

1879-2782

Reservoir computing is a powerful tool for forecasting turbulence because its simple architecture has the computational efficiency to handle high-dimensional systems. Its implementation, however, often requires full state-vector measurements and knowledge of the system nonlinearities. We use nonlinear projector functions to expand the system measurements to a high dimensional space and then feed them to a reservoir to obtain forecasts. We demonstrate the application of such reservoir computing networks on spatiotemporally chaotic systems, which model several features of turbulence. We show that using radial basis functions as nonlinear projectors enables complex system nonlinearities to be captured robustly even with only partial observations and without knowing the governing equations. Finally, we show that when measurements are sparse or incomplete and noisy, such that even the governing equations become inaccurate, our networks can still produce reasonably accurate forecasts, thus paving the way towards model-free forecasting of practical turbulent systems.

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

SCI ; EI

英语

第一 ; 通讯

National Natural Science Foundation of China["12002147","12050410247","11988102"] ; Shenzhen Science and Technology Program[KQTD20180411143441009] ; Department of Science and Technology of Guangdong Province[2020B1212030001] ; Research Grants Council of Hong Kong["16210419","16200220","16215521"]

Computer Science ; Neurosciences & Neurology

Computer Science, Artificial Intelligence ; Neurosciences

WOS:000938693400001

PERGAMON-ELSEVIER SCIENCE LTD

20230613550189

Chaotic systems ; Computational efficiency ; Control nonlinearities ; Forecasting ; Nonlinear equations ; Radial basis function networks ; Turbulence models

Artificial Intelligence:723.4 ; Control Systems:731.1 ; Systems Science:961

COMPUTER SCIENCE

2-s2.0-85147259261

Scopus

1.Guangdong Provincial Key Laboratory of Turbulence Research and Applications,Department of Mechanics and Aerospace Engineering,Southern University of Science and Technology,Shenzhen,518055,China
2.Guangdong-Hong Kong-Macao Joint Laboratory for Data-Driven Fluid Mechanics and Engineering Applications,Southern University of Science and Technology,Shenzhen,518055,China
3.Department of Mechanical and Aerospace Engineering,Hong Kong University of Science and Technology,Hong Kong
4.Guangdong-Hong Kong-Macao Joint Laboratory for Data-Driven Fluid Mechanics and Engineering Applications,Hong Kong University of Science and Technology,Hong Kong
5.Eastern Institute for Advanced Study,Ningbo,315200,China
6.Jiaxing Research Institute,Southern University of Science and Technology,Jiaxing,314031,China

Gupta，Vikrant,Li，Larry K.B.,Chen，Shiyi,et al. Model-free forecasting of partially observable spatiotemporally chaotic systems[J]. NEURAL NETWORKS,2023,160:297-305.

Gupta，Vikrant,Li，Larry K.B.,Chen，Shiyi,&Wan，Minping.(2023).Model-free forecasting of partially observable spatiotemporally chaotic systems.NEURAL NETWORKS,160,297-305.

Gupta，Vikrant,et al."Model-free forecasting of partially observable spatiotemporally chaotic systems".NEURAL NETWORKS 160(2023):297-305.