The Regionally-Implicit Discontinuous Galerkin Method: Improving the Stability of DG-FEM

TL;DR

This paper introduces the regionally implicit discontinuous Galerkin (RIDG) method, which enhances stability and removes polynomial degree dependence of time-step restrictions in DG-FEM for hyperbolic PDEs, enabling larger stable time-steps.

Contribution

The paper presents a novel RIDG method that extends the Lax-Wendroff DG approach by incorporating neighboring information in the predictor, significantly improving stability in multiple dimensions.

Findings

Removes polynomial degree dependence of time-step size.

Enhances stability for multi-dimensional problems.

Demonstrates improved efficiency through numerical studies.

Abstract

Discontinuous Galerkin (DG) methods for hyperbolic partial differential equations (PDEs) with explicit time-stepping schemes, such as strong stability-preserving Runge-Kutta (SSP-RK), suffer from time-step restrictions that are significantly worse than what a simple Courant-Friedrichs-Lewy (CFL) argument requires. In particular, the maximum stable time-step scales inversely with the highest degree in the DG polynomial approximation space and becomes progressively smaller with each added spatial dimension. In this work we introduce a novel approach that we have dubbed the regionally implicit discontinuous Galerkin (RIDG) method to overcome these small time-step restrictions. The RIDG method is based on an extension of the Lax-Wendroff DG (LxW-DG) method, which previously had been shown to be equivalent to a predictor-corrector approach, where the predictor is a locally implicit spacetime method (i.e., the predictor is something like a block-Jacobi update for a fully implicit spacetime DG method). The corrector is an explicit method that uses the spacetime reconstructed solution from the predictor step. In this work we modify the predictor to include not just local information, but also neighboring information. With this modification we show that the stability is greatly enhanced; in particular, we show that we are able to remove the polynomial degree dependence of the maximum time-step and show how this extends to multiple spatial dimensions. A semi-analytic von Neumann analysis is presented to theoretically justify the stability claims. Convergence and efficiency studies for linear and nonlinear problems in multiple dimensions are accomplished using a MATLAB code that can be freely downloaded.