Simulation and Data Assimilation in a Chaotic Dynamical System by Cellular Neural Networks

C. M. O. Oliveir (), A. M. Saraiva (), A. C. B. Delbem (), F. P. Härter (), G. G. Z. Lemos and H. F. Campos Velho ()
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C. M. O. Oliveir: University of Sao Paulo – USP
A. M. Saraiva: University of Sao Paulo – USP
A. C. B. Delbem: University of Sao Paulo – USP
F. P. Härter: Federal University of Pelotas – UFPel
G. G. Z. Lemos: National Institute for Space Research – INPE
H. F. Campos Velho: National Institute for Space Research – INPE

Chapter Chapter 17 in Integral Methods in Science and Engineering, 2026, pp 251-266 from Springer

Abstract: Abstract Weather and climate predictions are very important topics because of their impact on several activities of society. One essential feature of a prediction system is to compute the best initial condition for starting a forecasting cycle. The procedure to identify the best initial condition is a method by combining observation data from a dynamical system with data from a previous prediction, and this process is called data assimilation (DA). Some non-linear time evolution differential equations present dynamics very sensitive to any tiny changes of initial conditions, exhibiting a chaotic dynamic. Hence, our experiments applying Cell-NN are performed by using the classical Lorenz chaotic model. The methodology is described, where the Cell-NN is presented, then the Lorenz model is shown, and methods for data assimilation (variational and Cell-NN) are explained. The configuration of algorithms is introduced and results for numerical experiments are shown.

Date: 2026
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DOI: 10.1007/978-3-032-04458-7_17

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