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Journal Articles IEEE Journal of Multiscale and Multiphysics Computational Techniques Year : 2023

Reduced Order Modeling for Parameterized Electromagnetic Simulation Based on Tensor Decomposition

Abstract

We present a data-driven surrogate modeling for parameterized electromagnetic simulation. This method extracts a set of reduced basis (RB) functions from full-order solutions through a two-step proper orthogonal decomposition (POD) method. A mapping from the time/parameter to the principal components of the projection coefficients, extracted by canonical polyadic decomposition (CPD) , is approximated by a cubic spline interpolation (CSI) approach. The reduced-order model (ROM) is trained in the offline phase, while the RB solution of a new time/parameter value is recovered fast during the online phase. We evaluate the performance of the proposed method with numerical tests for the scattering of a plane wave by a 2-D multi-layer dielectric disk and a 3-D multi-layer dielectric sphere.
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hal-04402221 , version 1 (18-01-2024)

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Xiao-Feng He, Liang Li, Stéphane Lanteri, Kun Li. Reduced Order Modeling for Parameterized Electromagnetic Simulation Based on Tensor Decomposition. IEEE Journal of Multiscale and Multiphysics Computational Techniques, 2023, 8, pp.296-305. ⟨10.1109/JMMCT.2023.3301978⟩. ⟨hal-04402221⟩
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