Surrogate modeling of time-domain electromagnetic wave propagation via dynamic mode decomposition and radial basis function

被引:1
|
作者
Li, Kun [1 ]
Li, Yixin [1 ]
Li, Liang [2 ]
Lanteri, Stephane [3 ]
机构
[1] Southwestern Univ Finance & Econ, Sch Math, Chengdu, Peoples R China
[2] Univ Elect Sci & Technol China, Sch Math Sci, Chengdu, Peoples R China
[3] Univ Cote Azur, CNRS, Inna, LJAD, Nice, France
基金
中国国家自然科学基金;
关键词
Surrogate modeling; Non-intrusive model order reduction; Proper orthogonal decomposition; Dynamic mode decomposition; Radial basis function; PROPER ORTHOGONAL DECOMPOSITION; REDUCED-ORDER MODEL; NEURAL-NETWORKS;
D O I
10.1016/j.jcp.2023.112354
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
This work introduces an 'equation-free' non-intrusive model order reduction (NIMOR) method for surrogate modeling of time-domain electromagnetic wave propagation. The nested proper orthogonal decomposition (POD) method, as a prior dimensionality reduction technique, is employed to extract the time-and parameter-independent reduced basis (RB) functions from a collection of high-fidelity (HF) solutions (or snapshots) on a properly chosen training parameter set. A dynamic mode decomposition (DMD) method, resulting in a further dimension reduction of the NIMOR method, is then used to predict the reduced-order coefficient vectors for future time instants on the previous training parameter set. The radial basis function (RBF) is employed for approximating the reduced-order coefficient vectors at a given untrained parameter in the future time instants, leading to the applicability of DMD method to parameterized problems. A main advantage of the proposed method is the use of a multi-step procedure consisting of the POD, DMD and RBF techniques to accurately and quickly recover field solutions from a few large-scale simulations. Numerical experiments for the scattering of a plane wave by a dielectric disk, by a multi-layer disk, and by a 3-D dielectric sphere nicely illustrate the performance of the NIMOR method.& COPY; 2023 Elsevier Inc. All rights reserved.
引用
收藏
页数:23
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