Deep learning based source reconstruction method using asymmetric encoder–decoder​ structure and physics-induced loss

This paper proposes a novel deep learning (DL) based source reconstruction method (SRM). The proposed DL-based SRM employs the deep convolutional asymmetric encoder–decoder structure (DCAEDS), which only demands one-time single-frequency far-field measurement on EM scattered field as its input and f...

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Vydáno v:Journal of computational and applied mathematics Ročník 438; s. 115503
Hlavní autoři: Ng, Michael, Yao, He Ming
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.03.2024
Témata:
Convolutional neural network
Deep learning
Real time
Source reconstruction method
Deep learning
Source reconstruction method
Real time
Convolutional neural network
ISSN:0377-0427
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Shrnutí:This paper proposes a novel deep learning (DL) based source reconstruction method (SRM). The proposed DL-based SRM employs the deep convolutional asymmetric encoder–decoder structure (DCAEDS), which only demands one-time single-frequency far-field measurement on EM scattered field as its input and further predicts the equivalent source on target scatterers. During the offline training, an EM scattering simulator is designed to compute EM scattered field originated from the predicted equivalent source on target scatterers. The DCAEDS is combined with the EM scattering simulator to optimize the loss function, including two parts: (1) Data-induced loss, which directly evaluates the difference between the prediction of the proposed DCAEDS and the true-labelled equivalent source of target scatterers; (2) Physics-induced loss, which evaluates the difference between received EM scattered field and the computed EM scattered field originated from the prediction of DCAEDS. Moreover, the proposed DL-based SRM can overcome the limitation of conventional methods, involving high computation cost and strong ill-conditions. Consequently, the proposed DL-based SRM can realize the reconstruction of the equivalent current source with higher accuracy and lower computation complexity. Numerical examples illustrate the feasibility of the proposed DL-based SRM, which opens the new path for DL-based EM computation approaches.
ISSN:0377-0427
DOI:10.1016/j.cam.2023.115503