Call for Special Issue Papers

Reconfigurable metasurfaces and reflectarrays have emerged as key enabling technologies for next-generation electromagnetic systems, including 6G communications, wireless power transfer, sensing, imaging, and autonomous platforms. By tailoring the electromagnetic response of subwavelength unit cells, these architectures allow for unprecedented control of amplitude, phase, polarization, and wavefront shape, enabling functions previously difficult and/or costly using conventional antenna technologies. Also, programmable and reconfigurable metasurfaces are opening new possibilities for adaptive radar sensing and imaging, where dynamically controlled electromagnetic apertures can enhance target detection, imaging resolution, and environmental awareness

At the same time, artificial intelligence (AI) and digital-twin (DT) methodologies are becoming powerful tools for the design, optimization, and operation of complex electromagnetic systems. DTs are becoming popular for system design as they provide virtual representations of the physical EM system that is updated, validated, or synchronized using measurements, operational data, or hardware state information, and that can support prediction, calibration, diagnosis, optimization, or closed-loop control. Some examples are external control circuits, non-linearities, and 3rd party equipment insertions into larger systems for measurement-based calibration, compensation of fabrication tolerances and element failures for applications to real-time beamforming, metasurface reconfiguration, RIS-assisted channel modeling or sensing.

DTs have also found applications in imaging to detect early cancer cells by generating simulated images of abnormalities for insertion into deep learning networks. Similar methods can be used for identifying sub-micron defects in RF manufacturing followed by real-time reconfiguration during the manufacturing process. Coupled with AI/deep learning, these approaches have a growing potential for transformative impacts across the areas of RF sensing, radar and communication links.

Based on the above examples, AI-assisted inverse design, physics-informed neural networks, and hybrid data-driven/physics-based solvers can drastically accelerate the exploration of large design spaces, while digital electromagnetic twins can enable continuous synchronization between virtual models and physical metasurface or reflectarray platforms. These advances open new opportunities for closed-loop optimization, in-situ calibration, fault diagnosis, real-time reconfiguration, intelligent sensing and radar imaging applications.

The entire scientific community is invited to submit papers that fall within the scope of reconfigurable metasurfaces and reflectarrays with AI and DT technology. Also, the proposed Special Issue welcomes contributions that demonstrate how AI, deep learning or DT concepts advance metasurface and reflectarray designs, including their application into practical systems. Contributions are welcomed by the entire electromagnetics community. Authors of papers presented at the triennial 2026 URSI General Assembly and Scientific Symposium to be held in Krakow, Poland are also invited to contribute expanded versions of conference papers to this Special Issue.

This Special Issue aims to provide a focused venue for recent advances at the intersection of reconfigurable metasurfaces/reflectarrays and AI/DTs. Topics to be highlighted are:

• AI-assisted design, optimization, and control of metasurfaces and reflectarrays, including physics-informed neural networks, knowledge-guided deep learning, and AI-enhanced global optimizers for pattern synthesis, bandwidth enhancement, and polarization control.
• DT concepts for metasurface- and reflectarray-based systems, spanning high-fidelity EM modeling, model-measurement synchronization, and real-time co-simulation for communication, sensing, and wireless power transfer scenarios.
• Reconfigurable architectures and unit cells (e.g., varactor-loaded, liquid-crystal, MEMS, time-modulated, non-local or non-reciprocal designs) that enable dynamic beam shaping, near-field manipulation, and analog computation.
• Measurement, calibration, and error-modeling methods tailored to programmable metasurfaces and reflectarrays, including compact test ranges, production-line testing concepts, modeling of experimental uncertainties, and DT based correction techniques.
• Application-driven platforms where reconfigurable metasurfaces and reflectarrays are integrated with AI/ DT, such as:
  • UAV-enabled wireless power transfer and sensing, including beamforming
  • Indoor and vehicular channel emulation with RIS-assisted DTs,
  • Wearable and conformal metasurface antennas,
  • High-frequency (mmWave/THz/W-band) reconfigurable apertures for 6G and beyond.4

Data and Code Sharing

All authors are invited to share via IEEE Data Port (ieee-dataport.org) and IEEE Code Ocean (innovate.ieee.org/ieee-code-ocean) the used data and developed codes to enhance the reproducibility and visibility of the articles published in the Special Issue.

Keywords

• Reconfigurable Metasurfaces
• Reflectarrays
• Digital Twins
• Artificial Intelligence
• Physics-Informed Machine Learning
• Electromagnetic Optimization
• Intelligent RF Systems

Guest Editors

John L. Volakis (Lead), Florida International University, USA (This email address is being protected from spambots. You need JavaScript enabled to view it.)

Sembiam R. Rengarajan, California State University, USA (This email address is being protected from spambots. You need JavaScript enabled to view it.)

Christos G. Christodoulou, University of New Mexico, USA (This email address is being protected from spambots. You need JavaScript enabled to view it.)

Ahmad Hoorfar, Villanova University, USA (This email address is being protected from spambots. You need JavaScript enabled to view it.)

Filippo Costa, University of Pisa, Italy (This email address is being protected from spambots. You need JavaScript enabled to view it.)

Grzegorz Bogdan, Warsaw University of Technology, Poland (This email address is being protected from spambots. You need JavaScript enabled to view it.)

Important Dates

Submission Deadline: 31 January 2027

Final Decision: 31 July 2027

Publication Date: 31 October 2027