论文标题
使用动态模式分解来表征弹性拓扑状态
Characterization of elastic topological states using dynamic mode decomposition
论文作者
论文摘要
弹性拓扑状态由于其缺陷 - 免疫性质，在许多科学和工程领域中都在收到越来越多的意图，从而实现了振动控制和信息处理的应用。在这里，我们使用动态模式分解（DMD）介绍了弹性拓扑状态的数据驱动发现。 DMD频谱和DMD模式是从沿拓扑边界的相关状态的传播中检索出来的，在该状态下，DMD学到了它们的性质。诸如分类和预测之类的应用可以通过DMD的基本特征来实现。我们使用DMD模式证明了拓扑和传统超材料之间的分类。此外，由DMD模式启用的模型实现了沿给定接口的拓扑状态传播的预测。我们使用DMD表征拓扑状态的方法可以为材料物理和更广泛的晶格系统中数据驱动的发现铺平道路。
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.
