An inverse design paradigm of multi-functional elastic metasurface via data-driven machine learning. (February 2023)
- Record Type:
- Journal Article
- Title:
- An inverse design paradigm of multi-functional elastic metasurface via data-driven machine learning. (February 2023)
- Main Title:
- An inverse design paradigm of multi-functional elastic metasurface via data-driven machine learning
- Authors:
- Zhou, Weijian
Wang, Shuoyuan
Wu, Qian
Xu, Xianchen
Huang, Xinjing
Huang, Guoliang
Liu, Yang
Fan, Zheng - Abstract:
- Graphical abstract: Highlights: Propose a data-driven inverse design paradigm of multifunctional elastic metasurface. Demonstrating a dual-functional metasurface with defection and focusing at different frequencies. Abstract: 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.
- Is Part Of:
- Materials & design. Volume 226(2023)
- Journal:
- Materials & design
- Issue:
- Volume 226(2023)
- Issue Display:
- Volume 226, Issue 2023 (2023)
- Year:
- 2023
- Volume:
- 226
- Issue:
- 2023
- Issue Sort Value:
- 2023-0226-2023-0000
- Page Start:
- Page End:
- Publication Date:
- 2023-02
- Subjects:
- Acoustic metasurface -- Multi-functional -- Inverse design -- Machine learning -- Data-driven
Materials -- Periodicals
Engineering design -- Periodicals
Matériaux -- Périodiques
Conception technique -- Périodiques
Electronic journals
620.11 - Journal URLs:
- http://catalog.hathitrust.org/api/volumes/oclc/9062775.html ↗
http://www.sciencedirect.com/science/journal/02641275 ↗
http://www.sciencedirect.com/science/journal/02613069 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.matdes.2022.111560 ↗
- Languages:
- English
- ISSNs:
- 0264-1275
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 5393.974000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 26083.xml