Conditional Wasserstein generative adversarial networks applied to acoustic metamaterial design
Journal of the Acoustical Society of America
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.
San José State University
Peter Lai, Feruza Amirkulova, and Peter Gerstoft. "Conditional Wasserstein generative adversarial networks applied to acoustic metamaterial design" Journal of the Acoustical Society of America (2021): 4362-4374. https://doi.org/10.1121/10.0008929
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.