Superconvergence of ultra-weak discontinuous Galerkin methods for the linear Schr\"odinger equation in one dimension

TL;DR

This paper proves superconvergence properties of ultra-weak discontinuous Galerkin methods for the 1D linear Schrödinger equation, achieving higher accuracy at specific points and through post-processing, with theoretical and numerical validation.

Contribution

It establishes new superconvergence rates for UWDG methods applied to the Schrödinger equation, including superconvergence of derivatives and special points, using novel test functions and correction techniques.

Findings

Superconvergence of cell averages and fluxes depends on flux choices and polynomial degree.

Superconvergence of the DG solution towards a special projection is proven.

Post-processing enhances accuracy to order 2k.

Abstract

We analyze the superconvergence properties of ultra-weak discontinuous Galerkin (UWDG) methods with various choices of flux parameters for one-dimensional linear Schr\"odinger equation. In our previous work [10], stability and optimal convergence rate are established for a large class of flux parameters. Depending on the flux choices and if the polynomial degree $k$ is even or odd, in this paper, we prove $2k$ or $(2k-1)$-th order superconvergence rate for cell averages and numerical flux of the function, as well as $(2k-1)$ or $(2k-2)$-th order for numerical flux of the derivative. In addition, we prove superconvergence of $(k+2)$ or $(k+3)$-th order of the DG solution towards a special projection. At a class of special points, the function values and the first and second order derivatives of the DG solution are superconvergent with order $k+2, k+1, k$, respectively. The proof relies on the correction function techniques initiated in [8], and applied to [6] for direct DG (DDG) methods for diffusion problems. Compared with [6], Schr\"odinger equation poses unique challenges for superconvergence proof because of the lack of the dissipation mechanism from the equation. One major highlight of our proof is that we introduce specially chosen test functions in the error equation and show the superconvergence of the second derivative and jump across the cell interfaces of the difference between numerical solution and projected exact solution. This technique was originally proposed in [12] and is essential to elevate the convergence order for our analysis. Finally, by negative norm estimates, we apply the post-processing technique and show that the accuracy of our scheme can be enhanced to order $2k.$ Theoretical results are verified by numerical experiments.