Machine Learning of Mirror Skin Effects in the Presence of Disorder

The skin effect, which is the extreme sensitivity of the spectrum and eigenstates to the boundary condition, is a remarkable phenomenon of non-Hermitian systems and is currently being actively studied. In particular, the mirror skin effect, which is the protection of the skin effect by mirror symmet...

Full description

Saved in:
Bibliographic Details
Published in:Journal of the Physical Society of Japan 2021-05, Vol.90 (5), p.53703
Main Authors: Araki, Hiromu, Yoshida, Tsuneya, Hatsugai, Yasuhiro
Format: Article
Language:English
Subjects:
Boundary conditions
Eigenvectors
Machine learning
Neural networks
Phase diagrams
Robustness
Silicones
Skin effect
Symmetry
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The skin effect, which is the extreme sensitivity of the spectrum and eigenstates to the boundary condition, is a remarkable phenomenon of non-Hermitian systems and is currently being actively studied. In particular, the mirror skin effect, which is the protection of the skin effect by mirror symmetry, has recently been discovered. In this paper, we clarify the robustness of the mirror skin effect against disorder that disrupts the mirror symmetry. Specifically, we employed a neural network to elucidate the robustness of the skin effect. The neural network is useful because it systematically predicts the presence/absence of skin modes in the form of a large number of localized states around the edge. The trained neural network detects skin effects with high accuracy and clarifies the phase diagram of the model. We also calculate the probabilities the neural network obtained for each of the states. The above results were additionally confirmed by calculating the inverse participation ratio.
ISSN:0031-9015
1347-4073
DOI:10.7566/JPSJ.90.053703