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Rapid-prototyping plays a critical role in the design of antennas and related planar circuits for wireless communications, especially as we embrace the 5G/6G protocols going forward into the future. While there are a number of software modules commercially available for such rapid prototyping, often they are found to be not as reliable as desired, especially when they are based on approximate equivalent circuit models for various circuit components comprising the antenna system. Consequently, it becomes necessary to resort to the use of more sophisticated simulation techniques, based on full-wave solvers that are numerically rigorous, albeit computer-intensive. Furthermore, optimizing the dimensions of antennas and circuits to enhance the performance of the system is frequently desired, and this often exacerbates the problem since the simulation must be run a large number of times to achieve the performance goal—an optimized design. Consequently, it is highly desirable to develop accurate yet efficient techniques, both in terms of memory requirements and runtimes, to expedite the design process as much as possible. This is especially true when the antenna utilizes metamaterials and metasurfaces for their performance enhancement, as is often the case in modern designs. The purpose of this paper is to present strategies that address the bottlenecks encountered in the generation of Green’s Functions for layered media, especially in the millimeter wave frequency range where the dimensions of the antennas and the platforms upon which they are mounted can be several wavelengths in size. The paper is divided into two parts. Part-I covers the topics of construction of layered medium Green’s Function for millimeter wavelengths; the Equivalent Medium Approach (EMA) which obviates the need to construct Green’s Function for certain geometries; and the T-matrix approach for hybridizing the finite methods with the Method of Moments(MoM). In Part-II of this paper, we go on to discuss three other strategies for performance enhancement of CEM techniques: the Characteristic Basis Function Method (CBFM); mesh truncation for finite methods by using a new form of the Perfectly Matched Layer (PML); and GPU acceleration of MoM as well as FDTD (Finite Difference Time Domain) algorithms. The common theme between the two parts is the “performance enhancement” of CEM (Computational Electromagnetics) techniques, which provides the synergistic link between the two parts.

Developing numerical methods to solve dynamic electromagnetic problems has broad application prospects. In computational electromagnetics, traditional numerical methods are commonly used to deal with static electromagnetic problems. However, they can hardly be applied in the modeling of time-varying materials and moving objects. So far, the studies on numerical methods that can efficiently solve dynamic electromagnetic problems are still very limited. In this paper, a numerical method called the time-domain finite integration technique (TDFIT) is extended to tackle this problem via the introduction of time-varying iterative coefficients. In order to validate the effectiveness of the proposed algorithm, three numerical examples are demonstrated, including two microstrip structures with a time-varying medium and a rapidly rotating structure. The numerical results reveal that the time-varying medium can induce a nonlinear spectrum shift, and the radar cross section (RCS) of a rapidly rotating structure is highly dependent on the rotating speed. The proposed algorithm opens a new avenue for the exploration of many intriguing phenomena in fundamental physics, including frequency conversion and magnetless nonreciprocity. Meanwhile, it can also lead to a wide range of promising practical applications, such as active electron devices, space-time metamaterials, and hypersonic vehicles.

We investigate a case of automated energy-budget-aware optimization of the physical position of nodes (sensors) in a Wireless Body Area Network (WBAN). This problem has not been presented in the literature yet, as opposed to antenna and routing optimization, which are relatively well-addressed. In our research, which was inspired by a safety-critical application for firefighters, the sensor network consists of three nodes located on the human body. The nodes communicate over a radio link operating in the 2.4 GHz or 5.8 GHz ISM frequency band. Two sensors have a fixed location: one on the head (earlobe pulse oximetry) and one on the arm (with accelerometers, temperature and humidity sensors, and a GPS receiver), while the position of the third sensor can be adjusted within a predefined region on the wearer’s chest. The path loss between each node pair strongly depends on the location of the nodes and is difficult to predict without performing a full-wave electromagnetic simulation. Our optimization scheme employs evolutionary computing. The novelty of our approach lies not only in the formulation of the problem but also in linking a fully automated optimization procedure with an electromagnetic simulator and a simplified human body model. This combination turns out to be a computationally effective solution, which, depending on the initial placement, has a potential to improve performance of our example sensor network setup by up to about 20 dB with respect to the path loss between selected nodes.

The ACES-China 2022 symposium was successfully held in Xuzhou, China on July 28-31 2022. The conference chairs along with a dedicated team of guest editors edited this special issue to provide the whole technical community the opportunity to further explore the most significant contributions to the symposium. Seventeen papers are presented in this special issue. All have been carefully peer reviewed and we hope that you find this special issue a valuable and inspiring contribution to the development of applied computational electromagnetics.

The month of March is Women’s History Month. We all have heard and know that only a small percentage of electrical engineers are female. This percentage is even smaller for those that are experts in the field of applied computational electromagnetics. In this article, we are focusing on Dr. Ozlem Kilic, one of the well-known and established intellectuals in the field of computational electromagnetics. Dr. Kilic not only has been a role model for young students as an excellent researcher, a technical leader, and a prolific writer in this field, but also she has extensive experience in teaching and has taken many academic administrationroles.

A new model order reduction strategy based on the reduced-basis method is carried out in this work. Starting off from time-harmonic Maxwell's equations, a new representation of the original Maxwell system is developed. First, a reduced basis approximation allows for a reduced-order representation of electrodynamics in the frequency band of interest. As a result, the Kurokawa series representation for electromagnetics turns pretty much into a finite sum of dominant eigenresonances, which stand upon global eigenmodes of the Maxwell system. This gives rise to a linear dynamical system in electromagnetics and, after a proper arrangement, provides extremely useful physical information from which an electrical engineer can get actionable design insights. In this work, we use computational electromagnetics as an actual design tool and several realistic design applications will be considered during the presentation.

This study presents an efficient and accurate method to compute the bistatic scattering cross section of conductive wedges using the subdomain method. By introducing equivalent surface currents via the surface equivalence principle and applying Jacobi–Anger series to the plane‐wave phase factor, the proposed method reduces computational cost while maintaining high precision as demonstrated by numerical experiments. This method provides a practical framework for bistatic scattering analysis in complex geometries.

This letter proposes a super‐resolution (SR) neural network model for high‐contrast electromagnetic scattering problems. The model is designed to predict fine‐grid field distributions based on low‐cost coarse‐grid simulations. By integrating a spatial channel attention mechanism, the model enhances accuracy in capturing field discontinuities induced by strong scatterers. Additionally, a residual‐in‐residual architecture is incorporated to provide the network with sufficient depth for effective correction of dispersion errors. The efficiency and accuracy of the proposed model have been validated through numerical experiments. Comparative evaluations with a recently proposed electromagnetic SR network, supplemented by rigorous ablation studies, further demonstrate the superior performance of our approach in high‐contrast scenarios. This letter proposes a super‐resolution neural network model for high‐contrast electromagnetic scattering problems. The model is designed to predict fine‐grid field distributions based on low‐cost coarse‐grid simulations.

The data-science revolution is poised to transform the way photonic systems are simulated and designed. Photonic systems are, in many ways, an ideal substrate for machine learning: the objective of much of computational electromagnetics is the capture of nonlinear relationships in high-dimensional spaces, which is the core strength of neural networks. Additionally, the mainstream availability of Maxwell solvers makes the training and evaluation of neural networks broadly accessible and tailorable to specific problems. In this Review, we show how deep neural networks, configured as discriminative networks, can learn from training sets and operate as high-speed surrogate electromagnetic solvers. We also examine how deep generative networks can learn geometric features in device distributions and even be configured to serve as robust global optimizers. Fundamental data-science concepts framed within the context of photonics are also discussed, including the network-training process, delineation of different network classes and architectures, and dimensionality reduction. Neural networks can capture nonlinear relationships in high-dimensional spaces and are powerful tools for photonic-system modelling. This Review discusses how deep neural networks can serve as surrogate electromagnetic solvers, inverse modelling tools and global device optimizers.

In this paper, a volume scattering model for a wider variety of scatterers in vegetation remote sensing is proposed by utilizing computational electromagnetics in developing the scattering model of the scatterers. In this proposed model, both Equivalent Principle Algorithm (EPA) and Relaxed Hierarchical Equivalent Source Algorithm (RHESA) are coupled together as the computational electromagnetics method in calculating the backscattering cross section from the scatterers available in vegetation medium. This paper firstly presents the related work and its research gap, followed by the discussion on the proposed approach and the methodology in developing the model. This paper also proposes the technique to validate the developed model.