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...
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| Vydané v: | IEEE antennas and wireless propagation letters Ročník 22; číslo 4; s. 1 - 5 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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New York
IEEE
01.04.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1536-1225, 1548-5757 |
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| Abstract | 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. |
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| AbstractList | 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. This letter 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 electromagnetic (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. |
| Author | Yao, He Ming Jiang, Lijun Ng, Michael Zhang, Huan Huan |
| Author_xml | – sequence: 1 givenname: Huan Huan orcidid: 0000-0003-4579-832X surname: Zhang fullname: Zhang, Huan Huan organization: Xidian University, Xi'an, China – sequence: 2 givenname: He Ming orcidid: 0000-0003-2814-9539 surname: Yao fullname: Yao, He Ming organization: Department of Mathematics, The University of Hong Kong, Pokfulam Road, Hong Kong, China – sequence: 3 givenname: Lijun orcidid: 0000-0002-7391-6322 surname: Jiang fullname: Jiang, Lijun organization: Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China – sequence: 4 givenname: Michael orcidid: 0000-0001-6833-5227 surname: Ng fullname: Ng, Michael organization: Department of Mathematics, The University of Hong Kong, Pokfulam Road, Hong Kong, China |
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| Snippet | 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... This letter proposes a novel deep learning (DL) based fast solver for the electromagnetic forward (EMF) process. This proposed fast full-wave solver for EMF... |
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| SubjectTerms | 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 |
| Title | Fast Full Wave Electromagnetic Forward Solver Based on Deep Conditional Convolutional Autoencoders |
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