A posteriori error analysis for a space-time parallel discretization of parabolic partial differential equations

TL;DR

This paper develops an a posteriori error analysis framework for a space-time parallel discretization of parabolic PDEs, combining Parareal in time with domain decomposition in space, enabling efficient parallel solutions with error control.

Contribution

It introduces an adjoint-based error estimation method that distinguishes temporal and spatial discretization errors in a combined space-time parallel algorithm for parabolic PDEs.

Findings

The error estimator accurately quantifies discretization errors.

The analysis effectively separates errors from different algorithm components.

Numerical experiments validate the estimator's effectiveness.

Abstract

We construct a space-time parallel method for solving parabolic partial differential equations by coupling the Parareal algorithm in time with overlapping domain decomposition in space. The goal is to obtain a discretization consisting of "local" problems that can be solved on parallel computers efficiently. However, this introduces significant sources of error that must be evaluated. Reformulating the original Parareal algorithm as a variational method and implementing a finite element discretization in space enables an adjoint-based a posteriori error analysis to be performed. Through an appropriate choice of adjoint problems and residuals the error analysis distinguishes between errors arising due to the temporal and spatial discretizations, as well as between the errors arising due to incomplete Parareal iterations and incomplete iterations of the domain decomposition solver. We first develop an error analysis for the Parareal method applied to parabolic partial differential equations, and then refine this analysis to the case where the associated spatial problems are solved using overlapping domain decomposition. These constitute our Time Parallel Algorithm (TPA) and Space-Time Parallel Algorithm (STPA) respectively. Numerical experiments demonstrate the accuracy of the estimator for both algorithms and the iterations between distinct components of the error.