OPERATOR INFERENCE AND PHYSICS-INFORMED LEARNING OF LOW-DIMENSIONAL MODELS FOR INCOMPRESSIBLE FLOWS

被引:0
作者
Benner P. [1 ,2 ]
Goyal P. [1 ]
Heiland J. [1 ]
Duff I.P. [1 ]
机构
[1] Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg
[2] Fakultät für Mathematik, Otto-Von-Guericke-Universität, Magdeburg
来源
Electronic Transactions on Numerical Analysis | 2021年 / 56卷
关键词
Computational fluid dynamics; Incompressible flow; Navier-Stokes equations; Operator inference; Scientific machine learning;
D O I
10.1553/ETNA_VOL56S28
中图分类号
学科分类号
摘要
Reduced-order modeling has a long tradition in computational fluid dynamics. The ever-increasing significance of data for the synthesis of low-order models is well reflected in the recent successes of data-driven approaches such as Dynamic Mode Decomposition and Operator Inference. With this work, we discuss an approach to learning structured low-order models for incompressible flow from data that can be used for engineering studies such as control, optimization, and simulation. To that end, we utilize the intrinsic structure of the Navier-Stokes equations for incompressible flows and show that learning dynamics of the velocity and pressure can be decoupled, thus, leading to an efficient operator inference approach for learning the underlying dynamics of incompressible flows. Furthermore, we demonstrate the performance of the operator inference in learning low-order models using two benchmark problems and compare with an intrusive method, namely proper orthogonal decomposition, and other data-driven approaches. © 2021 Kent State University. All rights reserved.
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页码:28 / 51
页数:23
相关论文
共 52 条
[51]  
WILLS A., SCHON T. B., LJUNG L., NINNESS B., Identification of Hammerstein-Wiener models, Automatica, 49, pp. 70-81, (2013)
[52]  
YILDIZ S., GOYAL P., BENNER P., KARASOZEN B., Data-driven learning of reduced-order dynamics for a parametrized shallow water equation, (2020)