An inverse design paradigm of multi-functional elastic metasurface via data-driven machine learning

• DOI: 10.1016/j.matdes.2022.111560
• Publication Date: 2023-01-04T07:21:52Z
• Authors: Weijian Zhou; Shuoyuan Wang; Qian Wu; Xianchen Xu; Xinjing Huang; Guoliang Huang; Yang Liu; Zheng Fan
• Keywords: 0301 basic medicine; 03 medical and health sciences; Acoustic Metasurface; Inverse design; Machine learning; :Mechanical engineering [Engineering]; TA401-492; Acoustic metasurface; Data-driven; Materials of engineering and construction. Mechanics of materials; Inverse Design; Multi-functional

Elastic metasurfaces have become one of the most promising platforms for manipulating mechanical wavefronts with the striking feature of ultra-thin geometry. The conventional design of mechanical metasurfaces significantly relies on numerical, trial-and-error methods to identify structural parameters of the unit cells, which requires huge computational resources and could be extremely challenging if the metasurface is multi-functional. Machine learning technique provides another powerful tool for the design of multi-functional elastic metasurfaces because of its excellent capability in building nonlinear mapping relation between high-dimensional input data and output data. In this paper, a machine learning network is introduced to extract the complex relation between high-dimensional geometrical parameters of the metasurface unit and its high-dimensional dynamic properties. Based on a big dataset, the well-trained network can play the role of a surrogate model in the inverse design of a multi-functional elastic metasurface to significantly shorten the time for the design. Such method can be conveniently extended to design other multi-functional metasurfaces for the manipulation of optical, acoustical or mechanical waves.