论文标题
用于集合的复杂湍流系统的集合预测的物理知识的数据驱动算法
A Physics-Informed Data-Driven Algorithm for Ensemble Forecast of Complex Turbulent Systems
论文作者
论文摘要
开发了一种具有条件高斯统计量（PIDD-CG）的物理学数据驱动算法的新的合奏预测算法，以预测具有部分观测值的复杂湍流系统的概率密度函数（PDFS）的时间演变。 PIDD-CG算法将独特的多尺度统计闭合模型与极有效的非线性数据同化方案集成在一起，以表示PDF作为条件统计的混合物，这克服了高维系统的维度诅咒。每个条件统计集合成员的时间演变中的多尺度特征通过适当组合物理信息分析公式和复发性神经网络有效地捕获。信息指标被用作后者的损失函数，以更准确地校准具有强大波动的关键湍流信号。提出的算法成功地预测了具有间歇性，政权切换和极端事件的强烈湍流系统的瞬态和统计平衡的非高斯PDF。
A new ensemble forecast algorithm, named as the physics-informed data-driven algorithm with conditional Gaussian statistics (PIDD-CG), is developed to predict the time evolution of the probability density functions (PDFs) of complex turbulent systems with partial observations. The PIDD-CG algorithm integrates a unique multiscale statistical closure model with an extremely efficient nonlinear data assimilation scheme to represent the PDF as a mixture of conditional statistics, which overcomes the curse of dimensionality for high-dimensional systems. The multiscale features in the time evolution of each conditional statistics ensemble member effectively captured 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 calibrate the key turbulent signals with strong fluctuations. The proposed algorithm succeeds in forecasting both the transient and statistical equilibrium non-Gaussian PDFs of strongly turbulent systems with intermittency, regime switching and extreme events.
