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http://hdl.handle.net/10397/116087
| Title: | Bio-inspired acoustic metamaterials for traffic noise control : bridging the gap with machine learning | Authors: | Lu, JH Ding, S Ni, YQ Li, S |
Issue Date: | 2025 | Source: | Communications engineering, 2025, v. 4, 136 | Abstract: | Acoustic metamaterials (AMMs) represent a transformative approach to sound manipulation, capable of controlling acoustic waves in ways that are not possible with traditional materials. These materials, often inspired by biological structures, leverage complex geometries and innovative designs to enhance sound absorption and control. This review outlines the fundamentals of bio-inspired AMMs, discusses their design and performance characteristics, and highlights the challenges in translating these innovations into practical applications. We also explore the integration of machine learning (ML) techniques with bio-inspired design to optimize AMM for practical implementation. Finally, we propose future research directions aimed at developing broadband AMMs that effectively address the pressing issue of traffic noise, thereby enhancing the overall efficacy of noise control solutions. | Publisher: | Nature Publishing Group | Journal: | Communications engineering | EISSN: | 2731-3395 | DOI: | 10.1038/s44172-025-00470-x | Rights: | Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. © The Author(s) 2025 The following publication Lu, JH., Ding, S., Ni, YQ. et al. Bio-inspired acoustic metamaterials for traffic noise control: bridging the gap with machine learning. Commun Eng 4, 136 (2025) is available at https://doi.org/10.1038/s44172-025-00470-x. |
| Appears in Collections: | Journal/Magazine Article |
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|---|---|---|---|---|
| s44172-025-00470-x.pdf | 8.21 MB | Adobe PDF | View/Open |
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