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Electronics
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Inverse Problems and Optimization in Electromagnetic Systems
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Inverse Problems and Optimization in Electromagnetic Systems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Microwave and Wireless Communications".

Deadline for manuscript submissions: 15 June 2026 | Viewed by 1920

Special Issue Editors

Prof. Dr. Kai Li

E-Mail Website
Guest Editor
School of Information and Electronic Engineering, Zhejiang University, Hangzhou 310027, China
Interests: electromagnetic inverse scattering; computational electromagnetics
Special Issues, Collections and Topics in MDPI journals
Dr. Huiran Zeng

E-Mail Website
Guest Editor
Hangzhou Institution of Technology, Xidian University, Hangzhou 311200, China
Interests: numerical methods in electromagnetics; EM theory
Dr. Tong He

E-Mail Website
Guest Editor
Zhejiang Lab, Hangzhou 311121, China
Interests: EM theory; numerical methods in electromagnetics; antenna design and optimization

Special Issue Information

Dear Colleagues,

Electromagnetic (EM) systems are foundational to modern technology, enabling advancements in wireless communications, medical diagnostics, remote sensing, and non-destructive material evaluation. The performance and innovation in these areas are increasingly dependent on our ability to solve two classes of challenging problems: inverse problems and optimization problems. Inverse problems seek to determine the intrinsic properties or geometry of an object from external field measurements, while optimization problems aim to find the best possible design parameters to achieve a desired performance.

This Special Issue, "Inverse Problems and Optimization in Electromagnetic Systems," will focus on the latest theoretical advancements, novel computational algorithms, and pioneering applications in these interconnected fields. The scope is to create a comprehensive collection of high-quality research that addresses the entire spectrum of challenges, from mathematical formulation to practical implementation. We invite contributions on topics including, but not limited to, the following:

  • Novel algorithms for EM inverse scattering (e.g., deep learning-based, deterministic, and stochastic methods).
  • Advanced optimization techniques (e.g., topology optimization, genetic algorithms, swarm intelligence, multi-objective optimization) for the design of antennas, microwave circuits, and metamaterials.
  • Applications in microwave and optical imaging, non-destructive testing, geophysical exploration, and biomedical diagnostics.
  • Theoretical developments in regularization, uniqueness, and stability of EM inverse problems.
  • The synergy between physics-based models and data-driven machine learning approaches.
  • Hardware and software co-design and optimization for complex EM systems.

While many publications address electromagnetics or optimization separately, this Special Issue aims to uniquely bridge the gap between these disciplines. By presenting state-of-the-art research at this intersection, it will provide readers with a holistic view of current trends and future directions, fostering new collaborations and inspiring innovative solutions to real-world engineering challenges.

Prof. Dr. Kai Li
Dr. Huiran Zeng
Dr. Tong He
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • inverse problems
  • electromagnetic optimization
  • computational electromagnetics
  • antenna design
  • metamaterials
  • microwave imaging
  • machine learning in electromagnetics

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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19 pages, 1674 KB  
Article
Phaseless Characterization of Multilayered Media: Combining Interferometric Holography and a MUSIC-Based Approach
by Mario Del Prete, Raffaele Solimene, Loreto Di Donato and Maria Antonia Maisto
Electronics 2026, 15(7), 1496; https://doi.org/10.3390/electronics15071496 - 2 Apr 2026
Viewed by 390
Abstract
Millimeter-wave and sub-millimeter-wave techniques are widely used in non-destructive testing of multilayered materials due to their ability to penetrate non-conductive media and resolve dielectric stratifications. However, conventional thickness estimation methods suffer from an inherent resolution limit dictated by the available frequency bandwidth. In [...] Read more.
Millimeter-wave and sub-millimeter-wave techniques are widely used in non-destructive testing of multilayered materials due to their ability to penetrate non-conductive media and resolve dielectric stratifications. However, conventional thickness estimation methods suffer from an inherent resolution limit dictated by the available frequency bandwidth. In this paper, a MUSIC-based approach is proposed to achieve super-resolution localization of echoes in the reflective response of the structure under test. The method exploits the sparsity of the reflective response, similarly to compressive sensing approaches, while providing improved reconstruction accuracy. Moreover, the proposed strategy enables the retrieval of dielectric permittivities and layer thicknesses without resorting to complex nonlinear fitting procedures. Finally, the method operates on magnitude-only data, with phase information recovered through an interferometric holographic technique, making the proposed framework well-suited for cost-effective industrial applications. Full article
(This article belongs to the Special Issue Inverse Problems and Optimization in Electromagnetic Systems)
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17 pages, 3852 KB  
Article
Interpolation-Weighted TSVD for Sparse Array Microwave Tomography
by Zekun Zhang, Heng Liu, Fan Li and Ruide Li
Electronics 2026, 15(6), 1212; https://doi.org/10.3390/electronics15061212 - 13 Mar 2026
Viewed by 449
Abstract
In microwave imaging with finite antenna arrays, the limited number of array elements constrains spatial sampling and degrades reconstruction quality. To enlarge the aperture effectively, virtual antennas are usually adopted. However, it may lead virtual data to dominate the reconstruction process, thereby amplifying [...] Read more.
In microwave imaging with finite antenna arrays, the limited number of array elements constrains spatial sampling and degrades reconstruction quality. To enlarge the aperture effectively, virtual antennas are usually adopted. However, it may lead virtual data to dominate the reconstruction process, thereby amplifying artifacts. This work proposes an interpolation-weighted truncated singular value decomposition (IW-TSVD) framework that expands multistatic scattering matrix by using an integer interpolation factor. The proposed method preserves all physically measured antenna data and applies explicit weighting to virtual channels to suppress their influence. Simulations and hardware experiments show that IW-TSVD improves structural similarity index (SSIM), reduces the mean squared error (MSE), and suppresses artifacts compared with conventional TSVD and zero-padding-based interpolated TSVD, without increasing hardware complexity. Full article
(This article belongs to the Special Issue Inverse Problems and Optimization in Electromagnetic Systems)
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30 pages, 2680 KB  
Article
Diffusion Model Inverse Modeling and Applications to Microwave Filters
by Shu-Li Zhao, Jian-Fei Wu, Le-Dong Chen, Meng-Jun Wang and Zhi-Tao Xiao
Electronics 2026, 15(3), 527; https://doi.org/10.3390/electronics15030527 - 26 Jan 2026
Viewed by 708
Abstract
This paper presents a framework for inverse modeling of microwave filters based on a conditional diffusion model developed to address the intrinsic non-uniqueness of reconstructing coupling matrices from specified S-parameter responses. In the forward diffusion process, Gaussian noise is progressively added to the [...] Read more.
This paper presents a framework for inverse modeling of microwave filters based on a conditional diffusion model developed to address the intrinsic non-uniqueness of reconstructing coupling matrices from specified S-parameter responses. In the forward diffusion process, Gaussian noise is progressively added to the filter design variables, and a denoising network conditioned on the target electrical responses is trained to predict the injected noise at arbitrary diffusion steps. At inference, we initialize with Gaussian noise and execute the learned reverse denoising dynamics process; independent seeds yield diverse sets of physically feasible design-variable solutions that satisfy identical electrical-response constraints. Experiments on fourth- and sixth-order filters show that the proposed method outperforms multivalued neural networks (MVNNs) and conditional generative adversarial networks (CGANs) in prediction accuracy, solution diversity, and cumulative training cost, thereby providing a robust and efficient framework for inverse microwave-filter modeling. Full article
(This article belongs to the Special Issue Inverse Problems and Optimization in Electromagnetic Systems)
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