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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Veröffentlicht in:	IEEE antennas and wireless propagation letters Jg. 22; H. 4; S. 1 - 5
Hauptverfasser:	Zhang, Huan Huan, Yao, He Ming, Jiang, Lijun, Ng, Michael
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.04.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
Online-Zugang:	Volltext
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