Model order reduction for parameterized electromagnetic problems using matrix decomposition and deep neural networks
Xiao-Feng He, Liang Li, Stephane Lanteri, Kun Li

TL;DR
This paper introduces a non-intrusive model order reduction method combining reduced basis, convolutional autoencoders, and spline interpolation to efficiently solve parameterized electromagnetic scattering problems with high accuracy.
Contribution
It presents a novel hybrid MOR approach that integrates deep learning and matrix decomposition for rapid electromagnetic simulations, fully decoupling offline and online stages.
Findings
Achieves fast online solution retrieval with high accuracy.
Demonstrates effectiveness on 2-D dielectric scattering problems.
Ensures method validity through offline-online decoupling.
Abstract
A non-intrusive model order reduction (MOR) method for solving parameterized electromagnetic scattering problems is proposed in this paper. A database collecting snapshots of high-fidelity solutions is built by solving the parameterized time-domain Maxwell equations for some values of the material parameters using a fullwave solver based on a high order discontinuous Galerkin time-domain (DGTD) method. To perform a prior dimensionality reduction, a set of reduced basis (RB) functions are extracted from the database via a two-step proper orthogonal decomposition (POD) method. Projection coefficients of the reduced basis functions are further compressed through a convolutional autoencoder (CAE) network. Singular value decomposition (SVD) is then used to extract the principal components of the reduced-order matrices generated by CAE, and a cubic spline interpolation-based (CSI) approach is…
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