A stochastic method to compute the $L^2$ localisation landscape

Fuente: arXiv
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Autores principales: Kakoi, Masataka, Slevin, Keith
Formato: Preprint
Publicado: 2022
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author Kakoi, Masataka
Slevin, Keith
author_facet Kakoi, Masataka
Slevin, Keith
contents The $L^2$ localisation landscape of L. Herviou and J. H. Bardarson is a generalisation of the localisation landscape of M. Filoche and S. Mayboroda. We propose a stochastic method to compute the $L^2$ localisation landscape that enables the calculation of landscapes using sparse matrix methods. We also propose an energy filtering of the $L^2$ landscape which can be used to focus on eigenstates with energies in any chosen range of the energy spectrum. We demonstrate the utility of these suggestions by applying the $L^2$ landscape to Anderson's model of localisation in one and two dimensions, and also to localisation in a model of the quantum Hall effect.
format Preprint
id arxiv_https___arxiv_org_abs_2212_10768
institution arXiv
publishDate 2022
record_format arxiv
spellingShingle A stochastic method to compute the $L^2$ localisation landscape
Kakoi, Masataka
Slevin, Keith
Disordered Systems and Neural Networks
The $L^2$ localisation landscape of L. Herviou and J. H. Bardarson is a generalisation of the localisation landscape of M. Filoche and S. Mayboroda. We propose a stochastic method to compute the $L^2$ localisation landscape that enables the calculation of landscapes using sparse matrix methods. We also propose an energy filtering of the $L^2$ landscape which can be used to focus on eigenstates with energies in any chosen range of the energy spectrum. We demonstrate the utility of these suggestions by applying the $L^2$ landscape to Anderson's model of localisation in one and two dimensions, and also to localisation in a model of the quantum Hall effect.
title A stochastic method to compute the $L^2$ localisation landscape
topic Disordered Systems and Neural Networks
url https://arxiv.org/abs/2212.10768