Electromagnetic Scattering Problems Research Articles

Physics-informed Deep Learning to Solve 2D Electromagnetic Scattering Problems

The utilization of physics-informed deep learning (PI-DL) methodologies provides an approach to augment the predictive capabilities of deep learning (DL) models by constraining them with known physical principles. We utilize a PI-DL model called the deep operator network (DeepONet) to solve two-dimensional (2D) electromagnetic (EM) scattering problems. Numerical results demonstrate that the discrepancy between the DeepONet and conventional method of moments (MoM) is small, while maintaining computational efficiency.

Combined Field-Only Boundary Integral Equations for PEC Electromagnetic Scattering Problem in Spherical Geometries

Combined Field-Only Boundary Integral Equations for PEC Electromagnetic Scattering Problem in Spherical Geometries

Underdetermined Equation Model Combined with Improved Krylov Subspace Basis for Solving Electromagnetic Scattering Problems

Underdetermined Equation Model Combined with Improved Krylov Subspace Basis for Solving Electromagnetic Scattering Problems

Fast Analysis of Broadband Electromagnetic Scattering Problems by Combining Hyper Basis Functions-Based MoM With Compressive Sensing

Fast Analysis of Broadband Electromagnetic Scattering Problems by Combining Hyper Basis Functions-Based MoM With Compressive Sensing

Dual-Layer Compressive Sensing Scheme Incorporating Adaptive Cross Approximation Algorithm for Solving Monostatic Electromagnetic Scattering Problems

Dual-Layer Compressive Sensing Scheme Incorporating Adaptive Cross Approximation Algorithm for Solving Monostatic Electromagnetic Scattering Problems

Adaptive Cross Approximation Accelerates Compressive Sensing-based Method of Moments for Solving Electromagnetic Scattering Problems

Adaptive Cross Approximation Accelerates Compressive Sensing-based Method of Moments for Solving Electromagnetic Scattering Problems

A Direct Solution Scheme for Wide-Angle Electromagnetic Scattering Problems Using Compressive Sensing-based Method of Moments

A Direct Solution Scheme for Wide-Angle Electromagnetic Scattering Problems Using Compressive Sensing-based Method of Moments

Uniqueness Results for Some Inverse Electromagnetic Scattering Problems with Phaseless Far-Field Data

Consider three electromagnetic scattering models, namely, electromagnetic scattering by an elastic body, by a chiral medium, and by a cylinder at oblique incidence. We are concerned with the corresponding inverse problems of determining the locations and shapes of the scatterers from phaseless far-field patterns. There are certain essential differences from the usual inverse electromagnetic scattering problems, and some fundamental conclusions need to be proved. First, we show that the phaseless far-field data are invariant under the translation of the scatterers and prove the reciprocity relations of the scattering data. Then, we justify the unique determination of the scatterers by utilizing the reference ball approach and the superpositions of a fixed point source and plane waves as the incident fields. The proofs are based on the reciprocity relations, Green’s formulas, and the analyses of the wave fields in the reference ball.

On the Direct Electromagnetic Scattering Problem by an Impenetrable Partially Coated Obstacle Embedded in a Chiral Environment

On the Direct Electromagnetic Scattering Problem by an Impenetrable Partially Coated Obstacle Embedded in a Chiral Environment

The Electromagnetic Scattering Problem by a Cylindrical Doubly-Connected Domain at Oblique Incidence: An Inverse Problem

In this work, we examine the inverse problem to reconstruct the inner boundary of a cylindrical doubly-connected infinitely long medium from measurements of the scattered electromagnetic wave in the far-field. We consider the integral representation of the solution to derive a non-linear system of equations for the unknown radial function. We propose an iterative scheme using linearization and regularization techniques.

Characteristic Mode Analysis: Application to Electromagnetic Radiation, Scattering, and Coupling Problems

Characteristic Mode Analysis: Application to Electromagnetic Radiation, Scattering, and Coupling Problems

Wideband Sherman–Morrison–Woodbury Formula-Based Algorithm for Electromagnetic Scattering Problems

In this paper, a wideband Sherman-Morrison-Woodbury formula-based algorithm (WSMWA) is proposed to efficiently compute the wideband and wide-angle electromagnetic scattering problems. In the proposed algorithm, the standard adaptive cross approximation (ACA) decomposition is only performed at the highest frequency of the frequency band of interest to find the dominant basis functions for each far-block pair. Then, at any frequency within the entire frequency band, the approximate compression of the impedance matrix can be efficiently constructed by using the impedance interpolation method with the dominant basis functions selected at the highest frequency. As a result, the WSMWA avoids performing the standard ACA repeatedly and saves a lot of computational time in comparison with the conventional Sherman-Morrison-Woodbury formula-based algorithm (SMWA) for wideband and wide-angle applications. Numerical results for electromagnetic scattering are given to demonstrate the efficiency and accuracy of the proposed algorithm.

Solving Electromagnetic Inverse Scattering Problems in Inhomogeneous Media by Deep Convolutional Encoder–Decoder Structure

This communication proposes a novel deep learning (DL) approach to solve electromagnetic inverse scattering (EMIS) problems in inhomogeneous media. The conventional approaches for solving inhomogeneous EMIS problems generally have to consider inhomogeneous Green’s functions or conduct approximation operations to media, which inevitably introduces various challenges, including complex mathematical derivation, high computation cost, unavoidable nonlinearity, and even strong ill-posedness. To avoid these challenges, we propose a DL approach based on the complex-valued deep convolutional neural networks (DConvNets), which comprise the deep convolutional encoder–decoder (DCED) structure. Its training data are collected based on the simple synthetic dataset. While the scattered fields received in the measurement domain are utilized as the input for the encoder to extract feature fragments, the final output for the counterpart decoder is the contrasts (permittivities) of dielectric scatterers in the target domain. In this way, unlike the conventional methods, the unknown domain between the target domain and measurement domain never has to be considered to compute inhomogeneous Green’s functions. Consequently, the inhomogeneous EMIS problems could be solved with higher accuracy even for extremely high-contrast targets. Numerical examples illustrate the feasibility of the proposed DL approach. It acts as a new candidate for solving EMIS problems in inhomogeneous media.

Principal Component Analysis Accelerated the Iterative Convergence of the Characteristic Mode Basis Function Method for Analyzing Electromagnetic Scattering Problems

Principal Component Analysis Accelerated the Iterative Convergence of the Characteristic Mode Basis Function Method for Analyzing Electromagnetic Scattering Problems

Windowed Green Function MoM for Second-Kind Surface Integral Equation Formulations of Layered Media Electromagnetic Scattering Problems

This paper presents a second-kind surface integral equation method for the numerical solution of frequency-domain electromagnetic scattering problems by locally perturbed layered media in three spatial dimensions. Unlike standard approaches, the proposed methodology does not involve the use of layer Green functions. It instead leverages an indirect M\"uller formulation in terms of free-space Green functions that entails integration over the entire unbounded penetrable boundary. The integral equation domain is effectively reduced to a small-area surface by means of the windowed Green function method, which exhibits high-order convergence as the size of the truncated surface increases. The resulting (second-kind) windowed integral equation is then numerically solved by means of the standard Galerkin method of moments (MoM) using RWG basis functions. The methodology is validated by comparison with Mie-series and Sommerfeld-integral exact solutions as well as against a layer Green function-based MoM. Challenging examples including realistic structures relevant to the design of plasmonic solar cells and all-dielectric metasurfaces, demonstrate the applicability, efficiency, and accuracy of the proposed methodology.

A Modified HODLR Solver Based on Higher Order Basis Functions for Solving Electromagnetic Scattering Problems

In this letter, we present a novel fast direct solver based on a modified hierarchically off-diagonal low-rank (M-HODLR) matrix format with higher order basis functions for analyzing electromagnetic scattering by electrically large objects. The M-HODLR matrix compresses the far-group submatrices by an extended admissibility condition, which enhances the efficiency and accuracy of the original HODLR. To further alleviate the burden of the fast direct solution of the conventional surface integral equation (SIE), the higher order hierarchical vector basis (HOHVB) functions defined over a curved surface are employed. To account for HOHVB, an efficient assembly scheme based on patch coupling is developed to accelerate the adaptive across approximation implementation in M-HODLR. The proposed method can significantly reduce the computational time and memory cost of a conventional SIE solver. Several numerical examples are presented to validate the accuracy and efficiency of the proposed method in solving electrically large objects. Owing to the characteristics of HOHVB, different patch sizes or basis functions with different orders can be chosen according to the local geometry of the object. Therefore, the proposed method is very appealing for large- and multiscale simulations.

An Improved GMRES Method for Solving Electromagnetic Scattering Problems by MoM

Method of moments (MoM) is a classical numerical method for analyzing electromagnetic (EM) scattering from various objects. Generalized minimal residual (GMRES) is a kind of widely used solver for linear systems of equations generated by MoM, whose calculation complexity mainly includes two parts, matrix-vector multiplications and the orthogonalization procedure. In order to accelerate the solution of matrix equations arising in MoM, an efficient strategy for constructing nonorthonormal Krylov bases is proposed in this article, which can be introduced into standard GMRES and its variants (e.g., GMRES( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> ) and quasi-GMRES) and can be easily combined with the fast approaches for MOM (e.g., FMM and ACA). The principle of the method is described in detail, and the effectiveness is verified by numerical results.

Application of Mosaic-Skeleton Approximations of Matrices in the Physical Optics Method for Electromagnetic Scattering Problems

Application of Mosaic-Skeleton Approximations of Matrices in the Physical Optics Method for Electromagnetic Scattering Problems

Fast High-Order Algorithms for Electromagnetic Scattering Problem from Finite Array of Cavities in TE Case with High Wave Numbers

In this paper, fast high-order finite difference algorithms for solving the electromagnetic scattering from the finite array of two-dimensional rectangular cavities are proposed in TE polarization. The scattering problem from the cavity array is described as coupled Helmholtz equations with transparent boundary conditions on open apertures. Second-order and fourth-order schemes for solving the coupled systems are developed in TE polarization respectively. A special technique is applied to construct a fourth-order scheme for transparent boundary conditions. Further, we propose fast algorithms which can simplify the larger global system to a small linear interface system on the apertures of cavities. Numerical experiments show the validity and efficiency of the proposed fast algorithms for solving the scattering problem with high wave numbers.

Enhanced Supervised Descent Learning Technique for Electromagnetic Inverse Scattering Problems by the Deep Convolutional Neural Networks

This work proposes a novel deep learning (DL) framework to solve the electromagnetic inverse scattering (EMIS) problems. The proposed framework integrates the complex-valued deep convolutional neural network (DConvNet) into the supervised descent method (SDM) to realize both off-line training and on-line “imaging” prediction for EMIS. The offline training consists of two parts: 1) DConvNet training: the training dataset is created, and the proposed DConvNet is trained to realize the EM forward process and 2) SDM training: the trained DConvNet is integrated into the SDM framework, and the average descent directions between the initial prediction and the true label of SDM iterative schemes are learned based on the same dataset in part 1). In the online step, the contrasts (permittivities) reconstruction of scatterers is realized by the SDM iteration process based on learned descent directions, while its forward process is achieved by the trained complex-valued DConvNet. Ultimately, this framework provides a new perspective to integrate the prior information into the EMIS solving process with the maintained accuracy. Unlike the conventional SDM, the novel proposed framework can significantly shorten the computation and realize the real-time imaging. Various numerical examples and discussions are provided to demonstrate the efficiency and accuracy of the proposed novel framework.