Ibrahim Ayed, Emmanuel de Bézenac, Arthur Pajot, Julien Brajard, Patrick Gallinari
TL;DR本文研究使用数据驱动框架和神经网络来预测复杂的非线性时空过程，并表现出显著的改进。
Abstract
We consider the problem of forecasting complex, nonlinear space-time processes when observations provide only partial information of on the system's state. We propose a natural →