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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2408.08428 |
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| _version_ | 1866913469317513216 |
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| author | Bastawrous, Mary V. Chen, Zhi Ogren, Alexander C. Daraio, Chiara Rudin, Cynthia Brinson, L. Catherine |
| author_facet | Bastawrous, Mary V. Chen, Zhi Ogren, Alexander C. Daraio, Chiara Rudin, Cynthia Brinson, L. Catherine |
| contents | Manipulating the dispersive characteristics of vibrational waves is beneficial for many applications, e.g., high-precision instruments. architected hierarchical phononic materials have sparked promise tunability of elastodynamic waves and vibrations over multiple frequency ranges. In this article, hierarchical unit-cells are obtained, where features at each length scale result in a band gap within a targeted frequency range. Our novel approach, the ``hierarchical unit-cell template method,'' is an interpretable machine-learning approach that uncovers global unit-cell shape/topology patterns corresponding to predefined band-gap objectives. A scale-separation effect is observed where the coarse-scale band-gap objective is mostly unaffected by the fine-scale features despite the closeness of their length scales, thus enabling an efficient hierarchical algorithm. Moreover, the hierarchical patterns revealed are not predefined or self-similar hierarchies as common in current hierarchical phononic materials. Thus, our approach offers a flexible and efficient method for the exploration of new regions in the hierarchical design space, extracting minimal effective patterns for inverse design in applications targeting multiple frequency ranges. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_08428 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Phononic materials with effectively scale-separated hierarchical features using interpretable machine learning Bastawrous, Mary V. Chen, Zhi Ogren, Alexander C. Daraio, Chiara Rudin, Cynthia Brinson, L. Catherine Applied Physics Machine Learning Manipulating the dispersive characteristics of vibrational waves is beneficial for many applications, e.g., high-precision instruments. architected hierarchical phononic materials have sparked promise tunability of elastodynamic waves and vibrations over multiple frequency ranges. In this article, hierarchical unit-cells are obtained, where features at each length scale result in a band gap within a targeted frequency range. Our novel approach, the ``hierarchical unit-cell template method,'' is an interpretable machine-learning approach that uncovers global unit-cell shape/topology patterns corresponding to predefined band-gap objectives. A scale-separation effect is observed where the coarse-scale band-gap objective is mostly unaffected by the fine-scale features despite the closeness of their length scales, thus enabling an efficient hierarchical algorithm. Moreover, the hierarchical patterns revealed are not predefined or self-similar hierarchies as common in current hierarchical phononic materials. Thus, our approach offers a flexible and efficient method for the exploration of new regions in the hierarchical design space, extracting minimal effective patterns for inverse design in applications targeting multiple frequency ranges. |
| title | Phononic materials with effectively scale-separated hierarchical features using interpretable machine learning |
| topic | Applied Physics Machine Learning |
| url | https://arxiv.org/abs/2408.08428 |