Deterministic and probabilistic deep learning models for inverse design of broadband acoustic cloak
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computer Science Program
Applied Mathematics and Computational Science Program
KAUST Grant NumberBAS/1/1626-01-01
Permanent link to this recordhttp://hdl.handle.net/10754/665791
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AbstractConcealing 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.
CitationAhmed, 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.3.013142
SponsorsThe 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.
PublisherAmerican Physical Society (APS)
JournalPhysical Review Research
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