Abstract

This paper is concerned with an efficient numerical method for solving the 1D stationary Schrödinger equation in the highly oscillatory regime. Being a hybrid, analytical–numerical approach it does not have to resolve each oscillation, in contrast to standard schemes for ODEs. We build upon the WKB-based (named after the physicists Wentzel, Kramers, Brillouin) marching method from Arnold et al. (2011) and extend it in two ways: By comparing the O(h) and O(h2) methods from Arnold et al. (2011) we design an adaptive step size controller for the WKB method. While this WKB method is very efficient in the highly oscillatory regime, it cannot be used close to turning points. Hence, we introduce for such regions an automated methods switching, choosing between the WKB method for the oscillatory region and a standard Runge–Kutta–Fehlberg 4(5) method in smooth regions.A similar approach was proposed recently in [Handley et al. (2016), Agocs et al. (2020)], however, only for an O(h)-method. Hence, we compare our new strategy to their method on two examples (Airy function on the spatial interval [0,108] with one turning point at x=0 and on a parabolic cylinder function having two turning points), and illustrate the advantages of the new approach w.r.t. accuracy and efficiency.

Highlights

  • This paper deals with the numerical solution of the highly oscillatory 1D Schrodinger equation ε2φ′′(x) + a(x)φ(x) = 0, x ∈ R . (1.1) Here, < ε ≪ is the rescaledPlanck constant (ε := √ 2m )

  • We build upon the WKB-based marching method from [2] and extend it in two ways: By comparing the O(h) and O(h2) methods from [2] we design an adaptive step size controller for the WKB method

  • We find that the algorithm using WKB+Runge-Kutta-Fehlberg 4(5) (RKF45) made fewer steps (58 vs. 91) while producing a slightly lower global error at the same time

Read more

Summary

Introduction

This paper deals with the numerical solution of the highly oscillatory 1D Schrodinger equation ε2φ′′(x) + a(x)φ(x) = 0 , x ∈ R. and φ the (possibly complex valued) Schrodinger wave function. The grid limitation this work adds on top can there be reduced to at of the algorithm from [2] an adaptive step size control as well as a switching mechanism. This allows the algorithm to switch to a standard ODE method (e.g., Runge-Kutta) during the computation in order to avoid technical or efficiency problems in regions where the coefficient function a(x) is very small or equal to zero.

The WKB-marching method
Step size control and switching mechanism
The adaptive step size controller
The switching mechanism
The Runge-Kutta-WKB method
Numerical results
First example
Second Example
Findings
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call