Space-time goal-oriented reduced basis approximation for linear wave equation

TL;DR

This paper introduces a goal-oriented reduced basis method for linear wave equations that enhances output accuracy by focusing sampling on output error minimization, enabling rapid and reliable evaluations.

Contribution

It develops a novel goal-oriented POD-Greedy sampling procedure within the reduced basis framework for hyperbolic PDEs, improving output accuracy over standard methods.

Findings

Significant improvement in output accuracy with the new method

Efficient offline-online computational procedures

Effective for repeated rapid evaluations of input-output relationships

Abstract

In this paper, we study numerically the linear damped second-order hyperbolic partial differential equation (PDE) with affine parameter dependence using a goal-oriented approach by finite element (FE) and reduced basis (RB) methods. The main contribution of this paper is the "goal-oriented" proper orthogonal decomposition (POD)-Greedy sampling procedure within the RB approximation context. First, we introduce the RB recipe: Galerkin projection onto a space $Y_N$ spanned by solutions of the governing PDE at $N$ selected points in parameter space. This set of $N$ parameter points is constructed by the standard POD-Greedy sampling procedure already developed. Second, based on the affine parameter dependence, we make use of the offline-online computational procedures: in the offline stage, we generate the RB space; in the online stage, given a new parameter value, we calculate rapidly and accurately the space-time RB output of interest and its associated asymptotic error. The proposed goal-oriented POD-Greedy sampling procedure can now be implemented and will look for the parameter points such that it minimizes this (asymptotic) output error rather than the solution error (or, error indicator which is the dual norm of residual) as in the standard POD-Greedy procedure. Numerical results show that the new goal-oriented POD-Greedy sampling procedure improves significantly the accuracy of the space-time output computation in comparison with the standard POD-Greedy one. The method is thus ideally suited for repeated, rapid and reliable evaluation of input-output relationships within the space-time setting.