A Physics-Induced Deep Learning Scheme for Electromagnetic Inverse Scattering-Reference-Cited by-同舟云学术

A Physics-Induced Deep Learning Scheme for Electromagnetic Inverse Scattering

Published:2023 Issue: Volume: Page:1-21
ISSN:0018-9480
Container-title:IEEE Transactions on Microwave Theory and Techniques
language:
Short-container-title:IEEE Trans. Microwave Theory Techn.

Author:

Wu Zeyang¹^ORCID,Peng Yuexing¹^ORCID,Wang Peng²^ORCID,Wang Wenbo¹^ORCID,Xiang Wei³^ORCID

Affiliation:

1. Ministry of Education (MoE), School of Information and Communication Engineering, Key Laboratory of Universal Wireless Communication, Beijing University of Posts and Telecommunications, Beijing, China

2. Telecommunication Company in Kista, Stockholm, Sweden

3. School of Computing, Engineering and Mathematical Sciences, La Trobe University, Melbourne, VIC, Australia

Funder

National Natural Science Foundation of China

High-Level Talent Team Project of the New Coast of Qingdao New District

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Condensed Matter Physics,Radiation

Link

http://xplorestaging.ieee.org/ielx7/22/4359079/10214437.pdf?arnumber=10214437

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2. An Efficient Compressive Sensing-Based Method for Microwave Inverse Imaging Using Sparse Induced Current;IEEE Transactions on Instrumentation and Measurement;2024

3. IE-VAE: A Deep Learning Method for Solving Electromagnetic Inverse Scattering Problems Based on Variational Autoencoder;Lecture Notes in Computer Science;2024

4. Unrolled Microwave Imaging via Physics-assisted Deep Learning;2023 IEEE Conference on Antenna Measurements and Applications (CAMA);2023-11-15