Deterministic and probabilistic deep learning models for inverse design of broadband acoustic cloak
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PhysRevResearch.3.013142.pdf
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ArticleKAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionComputer Science Program
Applied Mathematics and Computational Science Program
KAUST Grant Number
BAS/1/1626-01-01Date
2021-02-12Submitted Date
2020-11-23Permanent link to this record
http://hdl.handle.net/10754/665791
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Concealing an object from incoming waves (light and/or sound) remained science fiction for a long time due to the absence of wave-shielding materials in nature. Yet, the invention of artificial materials and new physical principles for optical and sound wave manipulation translated this abstract concept into reality by making an object optically and acoustically “invisible.” Here, we present the notion of a machine learning driven acoustic cloak and demonstrate an example of such a cloak with a multilayered core-shell configuration. We develop deterministic and probabilistic deep learning models based on autoencoderlike neural network structure to retrieve the structural and material properties of the cloaking shell surrounding the object that suppresses scattering of sound in a broad spectral range, as if it was not there. The probabilistic model enhances the generalization ability of design procedure and uncovers the sensitivity of the cloak’s parameters on the spectral response for practical implementation. This proposal opens up avenues to expedite the design of intelligent cloaking devices for tailored spectral response and offers a feasible solution for inverse scattering problems.Citation
Ahmed, W. W., Farhat, M., Zhang, X., & Wu, Y. (2021). Deterministic and probabilistic deep learning models for inverse design of broadband acoustic cloak. Physical Review Research, 3(1). doi:10.1103/physrevresearch.3.013142Sponsors
The work described here is supported by King Abdullah University of Science and Technology (KAUST) Artificial Intelligence Initiative Fund and KAUST Baseline Research Fund No. BAS/1/1626-01-01.Publisher
American Physical Society (APS)Journal
Physical Review ResearcharXiv
2010.14866Additional Links
https://link.aps.org/doi/10.1103/PhysRevResearch.3.013142ae974a485f413a2113503eed53cd6c53
10.1103/physrevresearch.3.013142
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