标题： 通过结合并行水库计算与降维来提高时空混沌的预测 显示英文标题

标题： Improving the prediction of spatio-temporal chaos by combining parallel reservoir computing with dimensionality reduction

摘要： 水库计算机可用于预测由时空混沌系统产生的时序数据。通过并行使用多个水库，可以有效降低每个水库的输入维度，从而显示出更好的预测性能。类似地，可以通过将输入数据转换到一个低维潜在空间来进一步降低输入数据的维度。结合这两种方法，我们证明了使用降维后的潜在空间进行并行水库计算不仅降低了计算成本，而且对于小到中等规模的水库也带来了更好的预测结果。这种协同方法基于一维Kuramoto-Sivashinsky方程的预测进行了说明和评估。 显示英文摘要

摘要： Reservoir computers can be used to predict time series generated by spatio-temporal chaotic systems. Using multiple reservoirs in parallel has shown improved performances for these predictions, by effectively reducing the input dimensionality of each reservoir. Similarly, one may further reduce the dimensionality of the input data by transforming to a lower-dimensional latent space. Combining both approaches, we show that using dimensionality-reduced latent space predictions for parallel reservoir computing not only reduces computational costs, but also leads to better prediction results for small to medium reservoir sizes. This synergetic approach is illustrated and evaluated on the basis of the prediction of the one-dimensional Kuramoto-Sivashinsky equation.