A physics-informed data-driven algorithm for ensemble forecast of complex turbulent systems
A physics-informed data-driven algorithm for ensemble forecast of complex turbulent systems
复制标题
用于复杂湍流系统集合预测的物理信息数据驱动算法
DOI：
10.1016/j.amc.2023.128480
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
D. Qi
中科院分区：
文献类型：
--
作者：
N. Chen;D. Qi
提出了一种新的集合预报算法--物理信息数据驱动条件高斯统计预报算法(PIDD-CG)，用于预报部分观测条件下复杂湍流系统的概率密度函数。PIDD-CG算法将独特的多尺度统计闭包建模策略与高效的非线性数据同化方案相结合，以创建条件统计量的混合。将一种有效的数据驱动建模方法与主导的条件统计量相结合作为预测集成成员，可以显著降低恢复高维PDF的高昂计算成本。通过物理信息解析公式和递归神经网络的适当组合，有效地预测了这些条件统计集合在时间演化中的多尺度特征。对于后者，采用信息度量作为损失函数，以更准确地捕捉期望的湍流特征。该算法在具有间歇性、区域转换和极端事件的强湍流系统的暂态和统计平衡非高斯PDF中都显示出有效的预报性能。它还有助于发展有效的统计降阶模型来恢复和预测一大组多尺度复杂系统的大规模相干结构。
A new ensemble forecast algorithm, named the physics-informed data-driven algorithm with conditional Gaussian statistics (PIDD-CG), is developed to predict the probability density functions (PDFs) of complex turbulent systems with partial observations. The PIDD-CG algorithm integrates a unique multiscale statistical closure modeling strategy with a highly efficient nonlinear data assimilation scheme to create a mixture of conditional statistics. An effective data-driven modeling method is integrated with the dominant conditional statistics to serve as the forecast ensemble members that can significantly reduce the high computational cost of recovering high-dimensional PDFs. The multiscale features in the time evolution of these conditional statistics ensembles are effectively predicted by an appropriate combination of physics-informed analytic formulae and recurrent neural networks. An information metric is adopted as the loss function for the latter to more accurately capture the desirable turbulent features. The proposed algorithm displays effective forecasting performance in both the transient and statistical equilibrium non-Gaussian PDFs of strongly turbulent systems with intermittency, regime switching, and extreme events. It also facilitates the development of efficient statistical reduced-order models in recovering and predicting the large-scale coherent structures of a large group of multiscale complex systems.
影响因子：
3.1
作者：
R. Plant;G. Craig
通讯作者：
R. Plant;G. Craig
基于机器学习的湍流动力系统统计闭合模型
DOI：
10.1098/rsta.2021.0205
发表时间：
2022
期刊：
Physical and Engineering Sciences
影响因子：
--
作者：
Qi, Di;Harlim, John
通讯作者：
Harlim, John