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Low-rank matrix factorization method for multiscale simulations: A review
In this paper, a review of the low-rank factorization method is presented, with emphasis on
their application to multiscale problems. Low-rank matrix factorization methods exploit the …
their application to multiscale problems. Low-rank matrix factorization methods exploit the …
Surface susceptibility synthesis of metasurface holograms for creating electromagnetic illusions
A systematic approach is presented to exploit the rich field transformation capabilities of
Electromagnetic (EM) metasurfaces for creating a variety of illusions using the concept of …
Electromagnetic (EM) metasurfaces for creating a variety of illusions using the concept of …
A review on fast direct methods of surface integral equations for analysis of electromagnetic scattering from 3-D PEC objects
This paper reviews a series of fast direct solution methods for electromagnetic scattering
analysis, aiming to significantly alleviate the problems of slow or even non-convergence of …
analysis, aiming to significantly alleviate the problems of slow or even non-convergence of …
Strong admissibility skeletonization factorization for fast direct solution of electromagnetic scattering from conducting objects
Z Rong, M Jiang, Y Chen, L Lei… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
In this article, a fast direct solver based on strong admissibility skeletonization factorization
(SASF) is proposed for electromagnetic scattering from conducting objects. Different from the …
(SASF) is proposed for electromagnetic scattering from conducting objects. Different from the …
Fast antenna characterization with numerical expansion functions built with partial knowledge of the antenna and accelerated asymptotic methods
M Righero, G Giordanengo, L Ciorba… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
We describe a method to use numerical simulations concurrently with measurements (near-
field data) to characterize the radiation properties of an antenna (far-field) with a strongly …
field data) to characterize the radiation properties of an antenna (far-field) with a strongly …
Mixed-form nested approximation for wideband multiscale simulations
We propose a mixed “skeleton” and “equivalence” nested approximation method to
compress the impedance matrix of the method of moments for the wideband multiscale …
compress the impedance matrix of the method of moments for the wideband multiscale …
Fast nested cross approximation algorithm for solving large-scale electromagnetic problems
A fast nested cross approximation (NCA) algorithm is developed in this paper for solving
large-scale electromagnetic problems. Different from the existing NCA, the proposed method …
large-scale electromagnetic problems. Different from the existing NCA, the proposed method …
Fast O(N log N) Algorithm for Generating Rank-Minimized H2-Representation of Electrically Large Volume Integral Equations
In this article, we propose a fast algorithm to generate a rank-minimized-representation for
solving electrically large volume integral equations (VIEs). Unlike existing methods whose …
solving electrically large volume integral equations (VIEs). Unlike existing methods whose …
Fast direct surface integral equation solution for electromagnetic scattering analysis with skeletonization factorization
Z Rong, M Jiang, Y Chen, L Lei… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
A fast direct surface integral equation (SIE) solver based on a novel skeletonization
factorization scheme is proposed for electromagnetic scattering from electrically large and …
factorization scheme is proposed for electromagnetic scattering from electrically large and …
Nested Reduction Algorithms for Generating a Rank-Minimized ℋ2-Matrix From FMM for Electrically Large Analysis
In this work, we develop efficient algorithms to generate a rank-minimized H 2-matrix to
represent electrically large surface integral operators for a prescribed accuracy. We first …
represent electrically large surface integral operators for a prescribed accuracy. We first …