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Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing Approach
Physical Review Letters ( IF 8.1 ) Pub Date : 2018-01-12 00:00:00 , DOI: 10.1103/physrevlett.120.024102
Jaideep Pathak , Brian Hunt , Michelle Girvan , Zhixin Lu , Edward Ott

We demonstrate the effectiveness of using machine learning for model-free prediction of spatiotemporally chaotic systems of arbitrarily large spatial extent and attractor dimension purely from observations of the system’s past evolution. We present a parallel scheme with an example implementation based on the reservoir computing paradigm and demonstrate the scalability of our scheme using the Kuramoto-Sivashinsky equation as an example of a spatiotemporally chaotic system.

中文翻译:

基于数据的大型时空混沌系统的无模型预测:一种储层计算方法

我们证明了使用机器学习进行任意模型的时空混沌系统的无模型预测的有效性,该时空混沌系统具有任意大的空间范围和吸引子尺寸,完全是通过观察系统的过去演化而得出的。我们提出了一个基于油藏计算范例的并行实施方案示例,并展示了使用Kuramoto-Sivashinsky方程作为时空混沌系统示例的方案的可扩展性。
更新日期:2018-01-12
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