Inverse design of topological metaplates for fl exural waves with machine learning
Abstract
The mechanical analog to the topological insulators brings anomalous elastic wave properties which diversifies
classic wave functions for potential broad applications. To obtain topological mechanical wave states with
good quality at desired frequency ranges, it needs repetitive trials of different geometric parameters with tradi-
tional forward designs. In this work, we develop an inverse design of topological edge states for flexural wave
using machine learning method which is promising for instantaneous design. Nonlinear mapping function
from input targets to output desired parameters are adopted in artificial neural networks where the data sets for training are generated by the plane wave expansion method. Topological edge states are then realized and compared for different bandgap width conditions with such inverse designs, proving that wide bandgap can pro- mote the confinement of the topological edge states. Finally, direction selective propagations with sharp turns are further demonstrated as anomalous wave behaviors. The machine learning inverse design of topological states for flexural wave provides an efficient way to design practical devices with targeted needs for potential applications such as signal processing, sensing and energy harvesting.
Details
- Organisation(s)
-
Institute of Photonics
- External Organisation(s)
-
Tongji University
Universitat Jaume I
Bauhaus-Universität Weimar
- Type
- Article
- Journal
- Materials & Design
- Volume
- 199
- No. of pages
- 9
- Publication date
- 02.2021
- Publication status
- Published
- Peer reviewed
- Yes
- ASJC Scopus subject areas
- General Materials Science, Mechanics of Materials, Mechanical Engineering
- Electronic version(s)
-
https://doi.org/10.15488/14508 (Access:
Open
)
https://doi.org/10.1016/j.matdes.2020.109390 (Access: Open )
-
Details in the research portal "Research@Leibniz University"
