Computing spectra without solving eigenvalue problems

TL;DR

This paper introduces a deterministic method based on the localization landscape and effective potential to approximate eigenvalues and localized eigenfunctions of disordered Schr"odinger operators without solving eigenvalue problems.

Contribution

The paper presents a novel approach that predicts eigenfunction supports and eigenvalues efficiently using a single source problem, bypassing traditional eigenvalue computations.

Findings

Effective in predicting eigenfunction supports and eigenvalues

Works well across various random potential distributions

Limited theoretical justification but validated by extensive computations

Abstract

The approximation of the eigenvalues and eigenfunctions of an elliptic operator is a key computational task in many areas of applied mathematics and computational physics. An important case, especially in quantum physics, is the computation of the spectrum of a Schr\"odinger operator with a disordered potential. Unlike plane waves or Bloch waves that arise as Schr\"odinger eigenfunctions for periodic and other ordered potentials, for many forms of disordered potentials the eigenfunctions remain essentially localized in a very small subset of the initial domain. A celebrated example is Anderson localization, for which, in a continuous version, the potential is a piecewise constant function on a uniform grid whose values are sampled independently from a uniform random distribution. We present here a new method for approximating the eigenvalues and the subregions which support such localized eigenfunctions. This approach is based on the recent theoretical tools of the localization landscape and effective potential. The approach is deterministic, predicting quantities that depend sensitively on the particular realization, rather than furnishing statistical or probabilistic results about the spectrum associated to a family of potentials with a certain distribution. These methods, which have only been partially justified theoretically, enable the calculation of the locations and shapes of the approximate supports of the eigenfunctions, the approximate values of many of the eigenvalues, and of the eigenvalue counting function and density of states, all at the cost of solving a single source problem for the same elliptic operator. We study the effectiveness and limitations of the approach through extensive computations in one and two dimensions, using a variety of piecewise constant potentials with values sampled from various different correlated or uncorrelated random distributions.