Model-Free Prediction of Chaotic Systems Using High Efficient Next-generation Reservoir Computing

Understand

To predict the future evolution of dynamical systems purely from observations of the past data is of great potential application.

• In this work, a new formulated paradigm of reservoir computing is proposed for achieving model-free predication for both low-dimensional and very large spatiotemporal chaotic systems.
• Compared with traditional reservoir computing models, it is more efficient in terms of predication length, training data set required and computational expense.
• By taking the Lorenz and Kuramoto-Sivashinsky equations as two classical examples of dynamical systems, numerical simulations are conducted, and the results show our model excels at predication tasks than the latest reservoir computing methods.