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Identifying Topological Phase Transitions in Experiments Using Manifold Learning
- Source :
- Phys. Rev. Lett.,125,12,127401 (2020)
- Publication Year :
- 2021
-
Abstract
- We demonstrate the identification and classification of topological phase transitions from experimental data using Diffusion Maps: a nonlocal unsupervised machine learning method. We analyze experimental data from an optical system undergoing a topological phase transition and demonstrate the ability of this approach to identify topological phase transitions even when the data originates from a small part of the system, and does not even include edge states.<br />Comment: 17 p
- Subjects :
- Physics - Optics
Condensed Matter - Mesoscale and Nanoscale Physics
Subjects
Details
- Database :
- arXiv
- Journal :
- Phys. Rev. Lett.,125,12,127401 (2020)
- Publication Type :
- Report
- Accession number :
- edsarx.2104.03607
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1103/PhysRevLett.125.127401