Phononic materials with effectively scale-separated hierarchical features using interpretable machine learning

• URL: http://arxiv.org/abs/2408.08428v1
• Date: Thu, 15 Aug 2024 21:35:06 GMT
• Title: Phononic materials with effectively scale-separated hierarchical features using interpretable machine learning
• Authors: Mary V. Bastawrous, Zhi Chen, Alexander C. Ogren, Chiara Daraio, Cynthia Rudin, L. Catherine Brinson,
• Abstract summary: 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 approach offers a flexible and efficient method for the exploration of new regions in the hierarchical design space.
• Score: 57.91994916297646
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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