Special Issue 2: Machine Learning for Electromagnetic Sensing and Imaging
Editors: Zhun Wei, Feng Xu, Xudong Chen and Massimo Panella


◈ Special Issue Information:

This Special Issue aims to invite researchers to contribute original research papers, dealing with all aspects of machine learning in electromagnetics. Topics of interest include, but are not limited to:

  • Machine learning for remote sensing
  • Machine learning for electromagnetic imaging
  • Machine learning for electromagnetic inverse design
  • Electromagnetic intelligent sensing

Special Issue Editors:

Zhun Wei
Zhejiang University, China
Feng Xu
Fudan University, China
Xudong Chen
National University of Singapore, Singapore
Massimo Panella
University of Rome "La Sapienza'', Italy


Manuscipts Submission:

Manuscripts should be submitted online. This special issue title should be selected during the online submission process. All papers will be peer-reviewed. Accepted papers will be published online continuously (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. Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers).


Published Papers in This Special Issue:

A Review of Deep Learning Approaches for Inverse Scattering Problems (Invited Review)

By Xudong Chen, Zhun Wei, Maokun Li, and Paolo Rocca
Progress In Electromagnetics Research, Vol. 167, 67-81, 2020

Recent Advances in Transfer Function-Based Surrogate Optimization for EM Design (Invited)

By Wei Liu, Feng Feng, and Qijun Zhang
Progress In Electromagnetics Research, Vol. 172, 61-75, 2021

More papers to be added here ...