ELEDIA

ELEDIA Artificial Intelligence Research Blog is online!
The ELEDIA Artificial Intelligence Research Blog (E-AIR Blog) has been launched! Check out our latest advancements on Artificial Intelligence as applied…
Dr. R. J. MAILLOUX awarded "Bruno Kessler" Honorary Professor
The ELEDIA Research Center is pleased to announce that Dr. Robert J. MAILLOUX will receive the title of Honorary Professor…

We are pleased to announce a invited review paper on Deep Learning in the Progress In Electromagnetic Research Journal:

X. Chen, Z. Wei, M. Li, and P. Rocca, “A review of deep learning approaches for inverse scattering problems,” Progress In Electromagnetic Research, Invited Review Paper, Progress In Electromagnetics Research, vol. 167, 67-81, 2020 (DOI: 10.2528/PIER20030705).

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Abstract:
In recent years, deep learning (DL) is becoming an increasingly important tool for solvinginverse scattering problems (ISPs). This paper reviews methods, promises, and pitfalls of deep learningas applied to ISPs. More specifically, we review several state-of-the-art methods of solving ISPs withDL, and we also offer some insights on how to combine neural networks with the knowledge of theunderlying physics as well as traditional non-learning techniques. Despitethe successes, DL also has itsown challenges and limitations in solving ISPs. These fundamental questions are discussed, and possiblesuitable future research directions and countermeasures will be suggested.
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The paper can be downloaded at the following link www.doi.org/10.2528/PIER20030705