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
Hauptverfasser:	Nair, Siddharth, Walsh, Timothy F., Pickrell, Greg, Semperlotti, Fabio
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Netherlands Elsevier B.V 01.09.2023 Elsevier
Schlagworte:	Autoencoders Deep learning Inverse scattering Material design Remote sensing Deep learning Inverse scattering Material design Autoencoders Remote sensing
ISSN:	0045-7825, 1879-2138
Online-Zugang:	Volltext
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