A physics-guided machine learning for multifunctional wave control in active metabeams

Jiaji Chen

doi:10.1117/12.2658457

28 April 2023 A physics-guided machine learning for multifunctional wave control in active metabeams

Jiaji Chen

Proceedings Volume PC12483, Active and Passive Smart Structures and Integrated Systems XVII; PC1248302 (2023) https://doi.org/10.1117/12.2658457
Event: SPIE Smart Structures + Nondestructive Evaluation, 2023, Long Beach, California, United States

Abstract

We proposed a physics-guided machine-learning based inverse design approach for realizing multifunctional wave control in active metabeams connecting with negative capacitances. The transfer matrix method which relates the wave field and its derivative to carry the wave propagation information will be embedded in the ML network to construct the mapping between the input and output responses of the unit cell. After this network is well trained, global wave propagation behavior in the active metabeam can be accurately described by the concatenation of networks of each unit cells into a global stiffness matrix. We further apply the proposed network as a surrogate model for genetic algorithm on the inverse design of the metabeam for multifunctional wave control. Our proposed approach can not only be easily extended to design other types of active/passive metamaterials, but also provides some insights into optimization aided engineering in high-dimensional design space of metamaterials.

Conference Presentation

Citation Download Citation

Jiaji Chen "A physics-guided machine learning for multifunctional wave control in active metabeams", Proc. SPIE PC12483, Active and Passive Smart Structures and Integrated Systems XVII, PC1248302 (28 April 2023); https://doi.org/10.1117/12.2658457

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KEYWORDS

Machine learning

Metamaterials

Acoustics

Wave propagation

3D modeling

Associative arrays

Crystals

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