A deep learning approach for the inverse shape design of 2D acoustic scatterers

Siddharth Nair; Timothy F. Walsh; Gregory Pickrell; Fabio Semperlotti

doi:10.1117/12.2658207

25 April 2023 A deep learning approach for the inverse shape design of 2D acoustic scatterers

Siddharth Nair, Timothy F. Walsh, Gregory Pickrell, Fabio Semperlotti

Proceedings Volume 12488, Health Monitoring of Structural and Biological Systems XVII; 124881A (2023) https://doi.org/10.1117/12.2658207
Event: SPIE Smart Structures + Nondestructive Evaluation, 2023, Long Beach, California, United States

Abstract

In this study, we develop an end-to-end deep learning-based inverse design approach to determine the scatterer shape necessary to achieve a target acoustic field. This approach integrates non-uniform rational B-spline (NURBS) into a convolutional autoencoder (CAE) architecture while concurrently leveraging (in a weak sense) the governing physics of the acoustic problem. By utilizing prior physical knowledge and NURBS parameterization to regularize the ill-posed inverse problem, this method does not require enforcing any geometric constraint on the inverse design space, hence allowing the determination of scatterers with potentially any arbitrary shape (within the set allowed by NURBS). A numerical study is presented to showcase the ability of this approach to identify physically-consistent scatterer shapes capable of producing user-defined acoustic fields.

Conference Presentation

Citation Download Citation

Siddharth Nair, Timothy F. Walsh, Gregory Pickrell, and Fabio Semperlotti "A deep learning approach for the inverse shape design of 2D acoustic scatterers", Proc. SPIE 12488, Health Monitoring of Structural and Biological Systems XVII, 124881A (25 April 2023); https://doi.org/10.1117/12.2658207

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