Topology optimization of single-groove acoustic metasurfaces using genetic algorithms

Lin, Zibin; Wei, Wang; Xu, Weikai; Yang, Tianzhi

doi:10.1007/s00419-021-02084-z

Cited by 7 publications

(3 citation statements)

References 43 publications

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“…Previously, geometric optimization of local metasurfaces existed during independent and unrelated microstructures. [35,36] Whereas, all mutually independent geometric microstructures are interconnected as one nonlocal WAMs by the long-range coupling. 3) The essential mechanisms of local WAMs and nonlocal WAMs are so very different than the nonlocal design can be seen as an optimized configuration of the improved local design, rather than two different structures under the same design philosophy.…”

Section: Resultsmentioning

confidence: 99%

Experimental Evidence of High‐Efficiency Nonlocal Waterborne Acoustic Metasurfaces

2022

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Inspired by recent theoretical demonstrations of nonlocal waterborne acoustic metasurfaces (WAMs), herein experimental evidence of a WAM with highly nonlocal features for high‐efficient waterborne sound control is proposed. An easy‐to‐implement nonlocal metasurface with enhanced long‐range interaction is designed and the effects of fluid–solid interaction (FSI) are considered. Results show that the nonlocal WAMs are observed to manipulate underwater sound with enhanced efficiency, as high as 97% was experimentally achieved, compared with a maximum efficiency of 71% of the conventional local devices. Besides, the robustness against frequency fluctuations and coarse discretization of the nonlocal WAMs are observed. The proposed device is potentially applicable for underwater signal communication and high‐resolution ultrasonic imaging.

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Section: Resultsmentioning

confidence: 99%

Experimental Evidence of High‐Efficiency Nonlocal Waterborne Acoustic Metasurfaces

2022

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“…Long, Chen et al used genetic algorithms to respectively design metasurface structures for sound absorption [ 26 , 29 ]. Li, Lin, et al have respectively used machine learning for encoding metasurfaces to enable the modulation of the sound field by arranging these logical units into specific sequences [ 30 , 31 ]. These studies have taken advantage of the benefits of machine learning methods for model construction, which can help weaken complex physical mechanisms and reduce the need for model accuracy.…”

Section: Introductionmentioning

confidence: 99%

Research on local sound field intensity control technique in metasurface based on deep neural networks

Zhao,

Lv,

Huang

et al. 2024

PLoS ONE

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The use of tunable metasurface technology to realize the underwater tracking function of submarines, which is one of the hotspots and difficulties in submarine design. The structure-to-sound-field metasurface design approach is a highly iterative process based on trial and error. The process is cumbersome and inefficient. Therefore, an inverse design method was proposed based on parallel deep neural networks. The method took the global and local target sound field feature information as input and the metasurface physical structure parameters as output. The deep neural network was trained using a kernel loss function based on a radial basis kernel function, which established an inverse mapping relationship between the desired sound field to the metasurface physical structure parameters. Finally, the sound field intensity modulation at a localized target range was achieved. The results indicated that within the regulated target range, this method achieved an average prediction error of less than 5 dB for 92.9% of the sample data.

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“…Sajedian et al relied on a dual deep Q learning network (DDQN) to find the right material type and the best ensemble design [28]. Machine learning was also being used to encode metasurfaces, which manipulated the sound field by arranging logic units into specific sequences [29,30]. The above data-driven deep learning-based approach has contributed significantly to the work on the reverse design of metasurfaces.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Design Method for Acoustic Metasurface Dual-Feature Fusion

Lv,

Zhao,

Huang

et al. 2024

Materials

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Existing research in metasurface design was based on trial-and-error high-intensity iterations and requires deep acoustic expertise from the researcher, which severely hampered the development of the metasurface field. Using deep learning enabled the fast and accurate design of hypersurfaces. Based on this, in this paper, an integrated learning approach was first utilized to construct a model of the forward mapping relationship between the hypersurface physical structure parameters and the acoustic field, which was intended to be used for data enhancement. Then a dual-feature fusion model (DFCNN) based on a convolutional neural network was proposed, in which the first feature was the high-dimensional nonlinear features extracted using a data-driven approach, and the second feature was the physical feature information of the acoustic field mined using the model. A convolutional neural network was used for feature fusion. A genetic algorithm was used for network parameter optimization. Finally, generalization ability verification was performed to prove the validity of the network model. The results showed that 90% of the integrated learning models had an error of less than 3 dB between the real and predicted sound field data, and 93% of the DFCNN models could achieve an error of less than 5 dB in the local sound field intensity.

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Topology optimization of single-groove acoustic metasurfaces using genetic algorithms

Cited by 7 publications

References 43 publications

Experimental Evidence of High‐Efficiency Nonlocal Waterborne Acoustic Metasurfaces

Experimental Evidence of High‐Efficiency Nonlocal Waterborne Acoustic Metasurfaces

Research on local sound field intensity control technique in metasurface based on deep neural networks

Deep Learning-Based Design Method for Acoustic Metasurface Dual-Feature Fusion

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