论文标题
拓扑语音的无监督歧管聚类
Unsupervised Manifold Clustering of Topological Phononics
论文作者
论文摘要
由于缺乏通用拓扑不变性和结构模式的随机性，拓扑语音的分类是具有挑战性的。在这里，我们展示了无需任何先验知识的无监督的多种多样的学习，即使系统不完美或无序，也没有任何先验知识，也不具有监督培训。这是通过利用有限的语音晶格来描述振荡器之间相关性的真实空间投影操作员来实现的。我们体现了典型的语音系统中有效的无监督歧管聚类，包括带有随机耦合，无形的语音拓扑拓扑器，高阶语音拓扑状态和非敏感语音链的一维Su-Schrieffer-Heeger-Heeger-Heeger-Heeger-Heeger-Heeger型语音链。结果将激发对无监督的机器学习在拓扑语音设备及其他方面的应用的更多努力。
Classification of topological phononics is challenging due to the lack of universal topological invariants and the randomness of structure patterns. Here, we show the unsupervised manifold learning for clustering topological phononics without any priori knowledge, neither topological invariants nor supervised trainings, even when systems are imperfect or disordered. This is achieved by exploiting the real-space projection operator about finite phononic lattices to describe the correlation between oscillators. We exemplify the efficient unsupervised manifold clustering in typical phononic systems, including one-dimensional Su-Schrieffer-Heeger-type phononic chain with random couplings, amorphous phononic topological insulators, higher-order phononic topological states and non-Hermitian phononic chain with random dissipations. The results would inspire more efforts on applications of unsupervised machine learning for topological phononic devices and beyond.
