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.

翻译：利用机器学习来预测高维动态系统的行为需要有效的方法来学习基本物理模型。我们用一个机器学习结构来展示时空混乱预测,如果与下一代储油层计算机相结合,这种机器学习结构将显示最先进的性能,计算时间为10美元3至10美元4美元,培训过程和培训数据集的计算速度比其他机器学习算法要快10美元2倍。我们还利用模型的翻译对称来进一步降低计算成本和培训数据,每个模型的计算时间为10美元。