Identifying Topological Phase Transitions in Experiments Using Manifold Learning
Optics
2021-04-09 v1 Mesoscale and Nanoscale Physics
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.
Cite
@article{arxiv.2104.03607,
title = {Identifying Topological Phase Transitions in Experiments Using Manifold Learning},
author = {Eran Lustig and Or Yair and Ronen Talmon and Mordechai Segev},
journal= {arXiv preprint arXiv:2104.03607},
year = {2021}
}
Comments
17 p