Reduced Order Modeling for Parameterized Electromagnetic Simulation Based on Tensor Decomposition

• Published in: IEEE journal on multiscale and multiphysics computational techniques Vol. 8; pp. 296 - 305
• Main Authors: He, Xiao-Feng, Li, Liang, Lanteri, Stephane, Li, Kun
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: canonical polyadic decomposition; Computational modeling; cubic spline interpolation; Electromagnetic simulation; Engineering Sciences; Interpolation; Mathematical models; Mathematics; Matrix decomposition; model order reduction; Multilayers; Parameterization; Parameters; Plane waves; Proper Orthogonal Decomposition; Read only memory; Reduced order models; Splines (mathematics); Tensors; canonical polyadic decomposition; cubic spline interpolation; electromagnetic simulation; proper orthogonal decomposition; model order reduction
• ISSN: 2379-8815; 2379-8793; 2379-8815
• DOI: 10.1109/JMMCT.2023.3301978

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.