Learning spatiotemporal chaos using next-generation reservoir computing

Forecasting the behavior of high-dimensional dynamical systems using machine learning requires efficient methods to learn the underlying physical model. We demonstrate spatiotemporal chaos prediction using a machine learning architecture that, when combined with a next-generation reservoir computer, displays state-of-the-art performance with a computational time 10(3)-10(4) times faster for training process and training data set similar to 10(2) times smaller than other machine learning algorithms. We also take advantage of the translational symmetry of the model to further reduce the computational cost and training data, each by a factor of similar to 10. Published under an exclusive license by AIP Publishing.