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
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Zusammenfassung: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 Gauss-Newton method. This inversion algorithm is tested using both synthetic and experimental datasets. We achieve a 0.87% compression rate while maintaining high-quality reconstruction. The deep neural network renders nonlinear model compression, which largely reduces the number of unknowns; hence, it has higher computational efficiency. Furthermore, various prior knowledge that is difficult to describe with rigorous forms can be incorporated into inversion through training the neural network, which mitigates the ill-posedness of the inverse problem.
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ISSN:0018-926X
1558-2221
DOI:10.1109/TAP.2022.3195553