Fast Full Wave Electromagnetic Forward Solver Based on Deep Conditional Convolutional Autoencoders

This paper proposes a novel deep learning (DL) based fast solver for the electromagnetic forward (EMF) process. This proposed fast full-wave solver for EMF process is designed based on the deep conditional convolutional autoencoder (DCCAE), consisting of a complex-valued deep convolutional encoder n...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:IEEE antennas and wireless propagation letters Ročník 22; číslo 4; s. 1 - 5
Hlavní autoři: Zhang, Huan Huan, Yao, He Ming, Jiang, Lijun, Ng, Michael
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.04.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Coders
Convolutional Neural Network
Decoding
Deep Learning
Domains
Electromagnetic Forward Process
Electromagnetics
Feature extraction
Mathematical models
Real Time
Real-time systems
Solvers
Synthetic data
Training
Transmitters
ISSN:1536-1225, 1548-5757
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:This paper proposes a novel deep learning (DL) based fast solver for the electromagnetic forward (EMF) process. This proposed fast full-wave solver for EMF process is designed based on the deep conditional convolutional autoencoder (DCCAE), consisting of a complex-valued deep convolutional encoder network and its corresponding complex-valued deep convolutional decoder network. The encoder network makes use of the input consisting of the incident EM wave and the contrast (permittivities) distribution of the target domain, while the corresponding decoder network predicts the total EM field illuminated by the input incident EM wave. The training of the proposed DCCAE solver for EMF is merely based on the simple synthetic dataset. Thanks to its strong approximation capability, the proposed DCCAE can realize the prediction of the EM field of target domain by using the incident EM field and the distribution of contrasts (permittivities). Therefore, compared with conventional methods, the EMF problem could be solved with higher accuracy and the significant reduced computation time. Numerical examples have illustrated the feasibility of the newly proposed DL based EMF solver. The newly proposed DL-based EMF solver presents its excellent performance for the real-time online application.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1536-1225
1548-5757
DOI:10.1109/LAWP.2022.3224983