Phononic materials with effectively scale-separated hierarchical features using interpretable machine learning
<|reference_start|>Phononic materials with effectively scale-separated hierarchical features using interpretable 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.<|reference_end|>
@article{bastawrous2024phononic,
    title={Phononic materials with effectively scale-separated hierarchical
  features using interpretable machine learning},
    author={Mary V. Bastawrous, Zhi Chen, Alexander C. Ogren, Chiara Daraio,
  Cynthia Rudin, L. Catherine Brinson},
    journal={arXiv preprint arXiv:2408.08428},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.08428},
    primaryClass={physics.app-ph cs.LG}
}