Download Call for Papers (PDF)

Submission Deadline: 1 November 2021

Aims & Scope: Artificial Intelligence (AI) tools and methods, among others, include evolutionary algorithms (EAs), machine learning (ML), Ontologies, Artificial Immune Systems, and fuzzy inference system (FIS). Different aspects of the AI have been used in the literature from evolutionary algorithms to knowledge representation using ontologies. Moreover, the current 5G and the future 6G communications systems require new AI techniques and applications. These AI techniques like Nature-Inspired algorithms, Decision Trees, Random Forests, Support Vector Machines, Extreme Learning Machines, Gaussian Processes, Artificial Neural Networks (ANNs), and Deep Learning Networks (DNNs) are gaining popularity in AP community. Additionally, hybrid combinations of AI and problem specific methods are also emerging. For example, cutting-edge applications like the smart radio environment enabled by Reconfigurable Intelligent Surfaces (RISs) can become a reality using AI techniques.

We invite researchers to contribute original papers describing applications and experiences on the emerging trends of AI methods for solving and modeling problems in electromagnetics. The purpose of this special section is to publish high-quality research papers as well as review articles addressing recent advances on AI applications in electromagnetics.

Potential topics include but are not limited to the following:

• Evolutionary algorithms (EAs) for electromagnetics
• Machine learning techniques for electromagnetics
• Fuzzy inference system (FIS) for problems in electromagnetics
• AI for biomedical applications and wireless monitoring
• Surrogate models for electromagnetics
• Parallel computing techniques electromagnetics
• Hybrid techniques for electromagnetics
• Other innovative AI techniques for electromagnetics
• Smart EM environment
• Intelligent Reflecting Surfaces

Keywords:

1. Evolutionary algorithms
2. Machine learning
3. Fuzzy inference system
4. Deep Learning
5. Hybrid techniques
6. Surrogate modeling

Lead Guest Editor

Sotirios K. Goudos
Aristotle University of Thessaloniki, Greece
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Guest Editors

Dimitris E. Anagnostou
Heriot Watt University, UK
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Zikri Bayraktar
HSchlumberger-Doll Research Center, USA
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Sawyer D. Campbell
The Pennsylvania State University, USA
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Qiang Ren
Beihang University, China
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Paolo Rocca
University of Trento, Italy
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