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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| Veröffentlicht in: | Computer methods in applied mechanics and engineering Jg. 414; H. C; S. 116167 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Netherlands
Elsevier B.V
01.09.2023
Elsevier |
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| ISSN: | 0045-7825, 1879-2138 |
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| Abstract | 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. |
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| AbstractList | 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. |
| ArticleNumber | 116167 |
| Author | Pickrell, Greg Walsh, Timothy F. Semperlotti, Fabio Nair, Siddharth |
| Author_xml | – sequence: 1 givenname: Siddharth orcidid: 0000-0001-5798-4985 surname: Nair fullname: Nair, Siddharth organization: Ray W. Herrick Laboratories, School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA – sequence: 2 givenname: Timothy F. surname: Walsh fullname: Walsh, Timothy F. organization: Sandia National Laboratory, Albuquerque, NM 87185, USA – sequence: 3 givenname: Greg surname: Pickrell fullname: Pickrell, Greg organization: Sandia National Laboratory, Albuquerque, NM 87185, USA – sequence: 4 givenname: Fabio surname: Semperlotti fullname: Semperlotti, Fabio email: fsemperl@purdue.edu organization: Ray W. Herrick Laboratories, School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA |
| BackLink | https://www.osti.gov/biblio/2000180$$D View this record in Osti.gov |
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| CitedBy_id | crossref_primary_10_1016_j_ymssp_2025_113293 crossref_primary_10_1016_j_enganabound_2024_105813 crossref_primary_10_1016_j_wavemoti_2024_103371 crossref_primary_10_1088_2632_2153_ad134a crossref_primary_10_1016_j_jcp_2025_114211 crossref_primary_10_1007_s00466_023_02434_4 crossref_primary_10_1007_s00366_024_02038_3 crossref_primary_10_1016_j_cma_2025_117814 |
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| Keywords | Deep learning Inverse scattering Material design Autoencoders Remote sensing |
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