Efficient Prediction of Shallow-Water Acoustic Transmission Loss Using a Hybrid Variational Autoencoder–Flow Framework

Efficient Prediction of Shallow-Water Acoustic Transmission Loss Using a Hybrid Variational Autoencoder–Flow Framework

Efficient prediction of shallow-water acoustic transmission loss (TL) is crucial for underwater detection, recognition, and communication systems. Traditional physical modeling methods require repeated calculations for each new scenario in practical waveguide environments, leading to low computation...

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Bibliographic Details
Main Authors: Bolin Su, Haozhong Wang, Xingyu Zhu, Penghua Song, Xiaolei Li
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Journal of Marine Science and Engineering
Subjects:
sound field prediction
transmission loss
variational autoencoder
normalizing flow
generative models
Online Access:https://www.mdpi.com/2077-1312/13/7/1325
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