论文标题
用于地球物理流动中线性求解器的机器学习预处理
Machine-Learned Preconditioners for Linear Solvers in Geophysical Fluid Flows
论文作者
论文摘要
测试了机器学习方法是否可以用于预处理以提高线性求解器的性能 - 天气和气候模型的半无限，网格点模型方法的骨干。将机器学习方法嵌入线性求解器的框架内，绕过了通常会批评机器学习方法的潜在鲁棒性问题，因为线性求解器可确保达到足够的，预定的预定的准确性水平。该方法不需要事先获得常规的预处理，并且在复杂性和机器学习设计选择方面非常灵活。几种机器学习方法用于学习具有半平台时间播放的浅水模型的最佳预处理，在概念上与更复杂的气氛模型相似。机器学习的预处理与常规的预处理具有竞争力，即使在训练数据集的动态范围之外使用，也可以提供良好的结果。
It is tested whether machine learning methods can be used for preconditioning to increase the performance of the linear solver -- the backbone of the semi-implicit, grid-point model approach for weather and climate models. Embedding the machine-learning method within the framework of a linear solver circumvents potential robustness issues that machine learning approaches are often criticized for, as the linear solver ensures that a sufficient, pre-set level of accuracy is reached. The approach does not require prior availability of a conventional preconditioner and is highly flexible regarding complexity and machine learning design choices. Several machine learning methods are used to learn the optimal preconditioner for a shallow-water model with semi-implicit timestepping that is conceptually similar to more complex atmosphere models. The machine-learning preconditioner is competitive with a conventional preconditioner and provides good results even if it is used outside of the dynamical range of the training dataset.
