Publication Date

12-1-2021

Document Type

Article

Publication Title

Journal of the Acoustical Society of America

Volume

150

Issue

6

DOI

10.1121/10.0008929

First Page

4362

Last Page

4374

Abstract

This work presents a method for the reduction of the total scattering cross section (TSCS) for a planar configuration of cylinders by means of generative modeling and deep learning. Currently, the minimization of TSCS requires repeated forward modelling at considerable computer resources, whereas deep learning can do this more efficiently. The conditional Wasserstein generative adversarial networks (cWGANs) model is proposed for minimization of TSCS in two dimensions by combining Wasserstein generative adversarial networks with convolutional neural networks to simulate TSCS of configuration of rigid scatterers. The proposed cWGAN model is enhanced by adding to it a coordinate convolution (CoordConv) layer. For a given number of cylinders, the cWGAN model generates images of 2D configurations of cylinders that minimize the TSCS. The proposed generative model is illustrated with examples for planar uniform configurations of rigid cylinders.

Funding Sponsor

San José State University

Comments

This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. This article appeared in The Journal of the Acoustical Society of America, Volume 150, Issue 6, Pages 4362-4374, 2021 and may be found at https://doi.org/10.1121/10.0008929.

Department

Mechanical Engineering

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