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:	arXiv.org
Hauptverfasser:	Nair, Siddharth, Walsh, Timothy F, Pickrell, Greg, Semperlotti, Fabio
Format:	Paper
Sprache:	Englisch
Veröffentlicht:	Ithaca Cornell University Library, arXiv.org 27.02.2023
Schlagworte:	Artificial neural networks Context Deep learning Inverse design Inverse problems Machine learning Physics Regularization Remote sensing Robustness (mathematics) Sound fields
ISSN:	2331-8422
Online-Zugang:	Volltext
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