  We propose and demonstrate a generative deep learning approach for the shape
recognition of an arbitrary object from its acoustic scattering properties. The
strategy exploits deep neural networks to learn the mapping between the latent
space of a two-dimensional 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 non-unique
solution space is overcome by the multifrequency phaseless far-field patterns.
This inverse method is a powerful design tool that does not 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.
