论文标题
从嘈杂的观察中进行监督学习：将机器学习技术与数据同化结合
Supervised learning from noisy observations: Combining machine-learning techniques with data assimilation
论文作者
论文摘要
数据驱动的预测和物理 - 无知的机器学习方法近年来吸引了越来越多的兴趣，从而实现了预测范围的远远超出了混乱的动力学系统的期望。在另一条研究中，已经成功地使用了一系列研究数据，将预测模型及其固有的不确定性与传入的嘈杂观测结合在一起。我们在这里工作的关键思想是通过有明智地结合机器学习算法和数据同化来实现提高预测能力。我们将随机特征图的物理不合Snostic数据驱动的方法结合在一起，作为集合Kalman滤波器数据同化过程中的预测模型。通过合并传入的嘈杂观测来依次学习机器学习模型。我们表明，所获得的预测模型具有非常好的预测技能，而一旦训练，计算便宜。超出了预测的任务，我们表明我们的方法可用于生成可靠的集合，以进行概率预测以及学习多尺度系统中的有效模型封闭。
Data-driven prediction and physics-agnostic machine-learning methods have attracted increased interest in recent years achieving forecast horizons going well beyond those to be expected for chaotic dynamical systems. In a separate strand of research data-assimilation has been successfully used to optimally combine forecast models and their inherent uncertainty with incoming noisy observations. The key idea in our work here is to achieve increased forecast capabilities by judiciously combining machine-learning algorithms and data assimilation. We combine the physics-agnostic data-driven approach of random feature maps as a forecast model within an ensemble Kalman filter data assimilation procedure. The machine-learning model is learned sequentially by incorporating incoming noisy observations. We show that the obtained forecast model has remarkably good forecast skill while being computationally cheap once trained. Going beyond the task of forecasting, we show that our method can be used to generate reliable ensembles for probabilistic forecasting as well as to learn effective model closure in multi-scale systems.
