论文标题
使用下一代储层计算学习时空混乱
Learning Spatiotemporal Chaos Using Next-Generation Reservoir Computing
论文作者
论文摘要
预测使用机器学习的高维动力系统的行为需要有效的方法来学习基本的物理模型。我们使用机器学习体系结构证明了时空混乱的预测，该架构与下一代储层计算机相结合时，显示出最新的性能，计算时间$ 10^3-10^4美元，用于培训过程和培训数据集$ \ sim $ \ sim 10^2 $比其他机器学习算法小倍。我们还利用模型的翻译对称性来进一步降低计算成本和培训数据，每倍$ \ sim $ 10。
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.
