Learning Spatiotemporal Chaos Using Next-Generation Reservoir Computing
This work addresses efficient prediction of spatiotemporal chaos for applications in physics and engineering, representing a strong specific gain rather than a broad paradigm shift.
The paper tackled the problem of forecasting high-dimensional dynamical systems by introducing a machine learning architecture combined with next-generation reservoir computing, achieving state-of-the-art performance with computational time 10^3-10^4 times faster for training and training data set ~10^2 times smaller than other algorithms.
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 and training data, each by a factor of $\sim$10.