Learning Spatiotemporal Chaos Using Next-Generation Reservoir Computing. (arXiv:2203.13294v3 [cs.LG] UPDATED)

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 $\sim 10^2$ times
smaller than other machine learning algorithms. We also take advantage of the
translational symmetry of the model to further reduce the computational cost …

arxiv chaos computing generation learning