A Generative Deep Learning Approach for Shape Recognition of Arbitrary Objects from Phaseless Acoustic Scattering Data

Waqas Waseem Ahmed, Mohamed Farhat, Pai-Yen Chen, Xiangliang Zhang, Ying Wu

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


A generative deep learning approach for shape recognition of an arbitrary object from its acoustic scattering properties is proposed and demonstrated. The strategy exploits deep neural networks to learn the mapping between the latent space of a 2D acoustic object and the far-field scattering amplitudes. A neural network is designed as an adversarial autoencoder and trained via unsupervised learning to determine the latent space of the acoustic object. Important structural features of the object are embedded in lower-dimensional latent space which supports the modeling of a shape generator and accelerates the learning in the inverse design process. The proposed inverse design uses the variational inference approach with encoder- and decoder-like architecture where the decoder is composed of two pretrained neural networks: the generator and the forward model. The data-driven framework finds an accurate solution to the ill-posed inverse scattering problem, where nonunique solution space is overcome by the multifrequency phaseless far-field patterns. This inverse method is a powerful design tool that doesn't require complex analytical calculation and opens up new avenues for practical realization, automatic recognition of arbitrary-shaped submarines or large fish, and other underwater applications.
Original languageEnglish (US)
Pages (from-to)2200260
JournalAdvanced Intelligent Systems
StatePublished - Jan 20 2023

Bibliographical note

KAUST Repository Item: Exported on 2023-01-23
Acknowledged KAUST grant number(s): BAS/1/1626-01-01, OSR-2020-CRG9-4374
Acknowledgements: The work described in here was supported by King Abdullah University of Science and Technology (KAUST) Artificial Intelligence Initiative Fund, KAUST Office of Sponsored Research (OSR) under grant no. OSR-2020-CRG9-4374, and KAUST Baseline Research Fund no. BAS/1/1626-01-01.


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