Unsupervised Manifold Clustering of Topological Phononics

Autor:	Long, Yang, Ren, Jie, Chen, Hong
Rok vydání:	2020
Předmět:	Condensed Matter - Disordered Systems and Neural Networks Condensed Matter - Mesoscale and Nanoscale Physics Physics - Data Analysis Statistics and Probability
Zdroj:	Phys. Rev. Lett. 124, 185501 (2020)
Druh dokumentu:	Working Paper
DOI:	10.1103/PhysRevLett.124.185501
Popis:	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. Comment: 6 pages, 4 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2001.02661 Zobrazit plný text záznamu View this record from Arxiv