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Yang Y, Ji C, Deng K. Rapid design of metamaterials via multitarget Bayesian optimization. Ann Appl Stat 2021. [DOI: 10.1214/20-aoas1426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Yang Yang
- Department of Mathematical Sciences & Center for Statistical Science, Tsinghua University
| | - Chunlin Ji
- Kuang-Chi Institute of Advanced Technology, Shenzhen, China
| | - Ke Deng
- Center for Statistical Science and Department of Industrial Engineering, Tsinghua University
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Zheng B, Yang J, Liang B, Cheng JC. Inverse design of acoustic metamaterials based on machine learning using a Gauss–Bayesian model. JOURNAL OF APPLIED PHYSICS 2020; 128. [DOI: 10.1063/5.0012392] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
Acoustic metamaterials (AMs) have attracted a substantial amount of attention in recent decades where the parameter design plays an important role. However, conventional design methods generally rely on analytical physical models and require a very large number of evaluations of acoustic performance. Here, we propose and experimentally demonstrate an inverse-design method for AMs based on machine learning using a Gauss–Bayesian model. As a result of the cycle of training and prediction and the use of adaptive acquisition functions, this method allows the parameters of AMs to be efficiently designed for specific functionalities without the need for physical models. Considering the significance of low-frequency ventilated sound absorption, we present a design for a typical acoustic metamaterial absorber with multiple structural parameters that facilitate high sound absorption at low frequencies. In the design process, the parameters were adaptively adjusted to improve the sound absorption performance at low frequencies using only 37 evaluations, and this high absorption performance was verified by the agreement of numerical and experimental results. Because of its low cost, high flexibility, and independence from physical models, this method paves the way for tremendous opportunities in the design of various AMs for particular desired functionalities.
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Affiliation(s)
- Bin Zheng
- Collaborative Innovation Center of Advanced Microstructures and Key Laboratory of Modern Acoustics, MOE, Institute of Acoustics, Department of Physics, Nanjing University 1 , Nanjing 210093, China
- Collaborative Innovation Center of Advanced Microstructures, Nanjing University 2 , Nanjing 210093, China
| | - Jing Yang
- Collaborative Innovation Center of Advanced Microstructures and Key Laboratory of Modern Acoustics, MOE, Institute of Acoustics, Department of Physics, Nanjing University 1 , Nanjing 210093, China
- Collaborative Innovation Center of Advanced Microstructures, Nanjing University 2 , Nanjing 210093, China
| | - Bin Liang
- Collaborative Innovation Center of Advanced Microstructures and Key Laboratory of Modern Acoustics, MOE, Institute of Acoustics, Department of Physics, Nanjing University 1 , Nanjing 210093, China
- Collaborative Innovation Center of Advanced Microstructures, Nanjing University 2 , Nanjing 210093, China
| | - Jian-chun Cheng
- Collaborative Innovation Center of Advanced Microstructures and Key Laboratory of Modern Acoustics, MOE, Institute of Acoustics, Department of Physics, Nanjing University 1 , Nanjing 210093, China
- Collaborative Innovation Center of Advanced Microstructures, Nanjing University 2 , Nanjing 210093, China
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