Computing Eigenvalues and Eigenfunctions of Schrödinger Equations Using a Model Reduction Approach

Computing Eigenvalues and Eigenfunctions of Schrödinger Equations Using a Model Reduction Approach

Year:    2018

Communications in Computational Physics, Vol. 24 (2018), Iss. 4 : pp. 1073–1100

Abstract

We present a model reduction approach to construct problem dependent basis functions and compute eigenvalues and eigenfunctions of stationary Schrödinger equations. The basis functions are defined on coarse meshes and obtained through solving an optimization problem. We shall show that the basis functions span a low-dimensional generalized finite element space that accurately preserves the lowermost eigenvalues and eigenfunctions of the stationary Schrödinger equations. Therefore, our method avoids the application of eigenvalue solver on fine-scale discretization and offers considerable savings in solving eigenvalues and eigenfunctions of Schrödinger equations. The construction of the basis functions are independent of each other; thus our method is perfectly parallel. We also provide error estimates for the eigenvalues obtained by our new method. Numerical results are presented to demonstrate the accuracy and efficiency of the proposed method, especially Schrödinger equations with double well potentials are tested.

You do not have full access to this article.

Already a Subscriber? Sign in as an individual or via your institution

Journal Article Details

Publisher Name:    Global Science Press

Language:    English

DOI:    https://doi.org/10.4208/cicp.2018.hh80.08

Communications in Computational Physics, Vol. 24 (2018), Iss. 4 : pp. 1073–1100

Published online:    2018-01

AMS Subject Headings:    Global Science Press

Copyright:    COPYRIGHT: © Global Science Press

Pages:    28

Keywords:    Schrödinger equation eigenvalue problems model reduction two-level techniques problem dependent basis functions computational chemistry.

  1. A Multiscale Finite Element Method for the Schrödinger Equation with Multiscale Potentials

    Chen, Jingrun | Ma, Dingjiong | Zhang, Zhiwen

    SIAM Journal on Scientific Computing, Vol. 41 (2019), Iss. 5 P.B1115

    https://doi.org/10.1137/19M1236989 [Citations: 4]
  2. Efficient multiscale methods for the semiclassical Schrödinger equation with time-dependent potentials

    Chen, Jingrun | Li, Sijing | Zhang, Zhiwen

    Computer Methods in Applied Mechanics and Engineering, Vol. 369 (2020), Iss. P.113232

    https://doi.org/10.1016/j.cma.2020.113232 [Citations: 4]
  3. A Model Reduction Method for Multiscale Elliptic Pdes with Random Coefficients Using an Optimization Approach

    Hou, Thomas Y. | Ma, Dingjiong | Zhang, Zhiwen

    Multiscale Modeling & Simulation, Vol. 17 (2019), Iss. 2 P.826

    https://doi.org/10.1137/18M1205844 [Citations: 15]
  4. A Multiscale Reduced Basis Method for the Schrödinger Equation With Multiscale and Random Potentials

    Chen, Jingrun | Ma, Dingjiong | Zhang, Zhiwen

    Multiscale Modeling & Simulation, Vol. 18 (2020), Iss. 4 P.1409

    https://doi.org/10.1137/19M127389X [Citations: 1]