Publication Date
2022
Journal or Book Title
arXiv Preprint
Abstract
Elastic topological states have been receiving increased intention in numerous scientific and engineering fields due to their defect-immune nature, resulting in applications of vibration control and information processing. Here, we present the data-driven discovery of elastic topological states using dynamic mode decomposition (DMD). The DMD spectrum and DMD modes are retrieved from the propagation of the relevant states along the topological boundary, where their nature is learned by DMD. Applications such as classification and prediction can be achieved by the underlying characteristics from DMD. We demonstrate the classification between topological and traditional metamaterials using DMD modes. Moreover, the model enabled by the DMD modes realizes the prediction of topological state propagation along the given interface. Our approach to characterizing topological states using DMD can pave the way towards data-driven discovery of topological phenomena in material physics and more broadly lattice systems.
DOI
https://doi.org/10.48550/arXiv.2210.08333
License
UMass Amherst Open Access Policy
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Li, Shuaifeng; Kevrekidis, Panayotis G.; and Yang, Jinkyu, "Characterization of elastic topological states using dynamic mode decomposition" (2022). arXiv Preprint. 1326.
https://doi.org/10.48550/arXiv.2210.08333