GRIDS-Net: Inverse shape design and identification of scatterers via geometric regularization and physics-embedded deep learning

This study presents a deep learning based methodology for both remote sensing and design of acoustic scatterers. The ability to determine the shape of a scatterer, either in the context of material design or sensing, plays a critical role in many practical engineering problems. This class of inverse...

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Bibliographic Details
Published in:Computer methods in applied mechanics and engineering Vol. 414; no. C; p. 116167
Main Authors: Nair, Siddharth, Walsh, Timothy F., Pickrell, Greg, Semperlotti, Fabio
Format: Journal Article
Language:English
Published: Netherlands Elsevier B.V 01.09.2023
Elsevier
Subjects:
Autoencoders
Deep learning
Inverse scattering
Material design
Remote sensing
Deep learning
Inverse scattering
Material design
Autoencoders
Remote sensing
ISSN:0045-7825, 1879-2138
Online Access:Get full text
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Summary:This study presents a deep learning based methodology for both remote sensing and design of acoustic scatterers. The ability to determine the shape of a scatterer, either in the context of material design or sensing, plays a critical role in many practical engineering problems. This class of inverse problems is extremely challenging due to their high-dimensional, nonlinear, and ill-posed nature. To overcome these technical hurdles, we introduce a geometric regularization approach for deep neural networks (DNN) based on non-uniform rational B-splines (NURBS) and capable of predicting complex 2D scatterer geometries in a parsimonious dimensional representation. Then, this geometric regularization is combined with physics-embedded learning and integrated within a robust convolutional autoencoder (CAE) architecture to accurately predict the shape of 2D scatterers in the context of identification and inverse design problems. An extensive numerical study is presented in order to showcase the remarkable ability of this approach to handle complex scatterer geometries while generating physically-consistent acoustic fields. The study also assesses and contrasts the role played by the (weakly) embedded physics in the convergence of the DNN predictions to a physically consistent inverse design.
Bibliography:NA0003525
USDOE National Nuclear Security Administration (NNSA)
ISSN:0045-7825
1879-2138
DOI:10.1016/j.cma.2023.116167