A Nonlinear Model Compression Scheme Based on Variational Autoencoder for Microwave Data Inversion

We present an inversion algorithm with a deep-learning-based model compression scheme. Models are described with latent parameters of a trained variational autoencoder (VAE) neural network. Given observed data, latent parameters are inverted by minimizing the data misfit cost function using the Gaus...

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Veröffentlicht in:	IEEE transactions on antennas and propagation Jg. 70; H. 11; S. 11059 - 11069
Hauptverfasser:	Guo, Rui, Lin, Zhichao, Li, Maokun, Yang, Fan, Xu, Shenheng, Abubakar, Aria
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.11.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Artificial neural networks Computational modeling Cost function Data models Decoding Deep learning Inverse problems inverse scattering inversion algorithm Machine learning Mathematical models model compression Neural networks Newton methods Parameters Permittivity Training variational autoencoder
ISSN:	0018-926X, 1558-2221
Online-Zugang:	Volltext
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