AI-Enhanced Electromagnetic Sensing and Inverse Imaging

Dear Colleagues,

Advances in electromagnetic sensing and imaging stand at the crossroads of physics, engineering, data science, and applications. With the rise of artificial intelligence, machine learning, and data-driven modelling, new opportunities are emerging to address long-standing inverse problems, boost sensitivity, improve reconstruction, enable real-time monitoring, and enhance diagnostics across environmental, industrial, biomedical, nondestructive testing, and material domains.

The purpose of this Special Issue is to provide a platform for original research, comprehensive reviews, and tutorial articles that explore the synergy of AI (including deep learning, data fusion, and model-based learning) with electromagnetic sensing, measurement, modelling, and inverse imaging. We invite contributions that span from theory to algorithms, simulation, and experiment, including applications such as remote sensing, microwave/millimetre/sub-terahertz imaging, ground-penetrating radar, antenna arrays, material characterisation, biomedical EM, structural health monitoring, smart sensing systems, optimisation of sensor/actuator networks, and real-world deployments.

Areas of interest include, but are not limited to, the following:

• AI-enabled microwave, millimetre, and terahertz sensing systems;
• Data-driven inverse modelling of electromagnetic fields and material properties;
• Hybrid physics-based and machine learning methods in electromagnetic imaging;
• Optimisation of sensor/antenna networks for improved electromagnetic measurement;
• Real-time monitoring and diagnostics using electromagnetic sensing + AI;
• Environmental monitoring, nondestructive evaluation, and biomedical applications of EM imaging;
• Simulation, numerical methods, and open-source tools with embedded machine learning workflows;
• Case studies of electromagnetic sensing systems enhanced with AI/ML for novel applications;
• Ethical, reproducibility, and explainability aspects of AI in electromagnetic systems.

We welcome both theoretical and experimental contributions, as well as review and tutorial papers. All manuscripts will undergo peer review, and accepted papers will be published open access.

We look forward to hearing from you.

Dr. Noushin Karimian
Dr. Sunday Ekpo
Guest Editors

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