A reservoir computer (RC) is a type of simplified recurrent neural network architecture that has demonstrated success in the prediction of spatiotemporally chaotic dynamical systems. A further advantage of RC is that it reproduces intrinsic dynamical quantities essential for its incorporation into numerical forecasting routines such as the ensemble Kalman filter -- used in numerical weather prediction to compensate for sparse and noisy data. We explore here the architecture and design choices for a "best in class" RC for a number of characteristic dynamical systems, and then show the application of these choices in scaling up to larger models using localization. Our analysis points to the importance of large scale parameter optimization. We also note in particular the importance of including input bias in the RC design, which has a significant impact on the forecast skill of the trained RC model. In our tests, the the use of a nonlinear readout operator does not affect the forecast time or the stability of the forecast. The effects of the reservoir dimension, spinup time, amount of training data, normalization, noise, and the RC time step are also investigated. While we are not aware of a generally accepted best reported mean forecast time for different models in the literature, we report over a factor of 2 increase in the mean forecast time compared to the best performing RC model of Vlachas et.al (2020) for the 40 dimensional spatiotemporally chaotic Lorenz 1996 dynamics, and we are able to accomplish this using a smaller reservoir size.
翻译:储油层计算机(RC)是一种简化的经常性神经网络结构,它证明成功地预测了时空混乱的动态系统。 RC的另一个优势是,它复制了内在动态数量,这是将它纳入数字预报常规所必不可少的,例如用于数字天气预测以弥补稀疏和繁杂数据的通灵卡尔曼过滤器。 我们在这里探讨一些典型动态系统的“在阶级中最优秀”RC的架构和设计选择,然后展示这些选择的应用,以利用本地化扩大模型。 我们的分析指出大规模参数优化的重要性。我们还注意到,在RC的设计中包括输入偏差的重要性,这对经过训练的RC模型的预测技能有重大影响。 在我们的测试中,使用非线性读取操作器并不影响预测的时间或预报的稳定性。 储油层的尺寸、递增时间、培训数据的数量、正常化、噪音和RC时间步骤的影响也得到了调查。 虽然我们不知道,在LoRC模型中的40级和RFSV模型中,我们用最接近的预测时间比20年的RB模型,我们报告在使用最接近于20年的RB模型。