Refined a posteriori error estimation for classical and pressure-robust Stokes finite element methods
P. L. Lederer, C. Merdon, J. Sch\"oberl

TL;DR
This paper develops a new a posteriori error estimator for pressure-robust Stokes finite element methods, focusing on the divergence-free part of the source term to improve robustness and efficiency, especially in pressure-dominant cases.
Contribution
It introduces a novel error estimator based on the curl of the right-hand side, enhancing pressure-robustness and reliability of error control in Stokes FEM.
Findings
The estimator is reliable and efficient for pressure-robust methods.
Numerical tests confirm improved error estimation in pressure-dominant scenarios.
The approach is applicable to Taylor--Hood and mini finite element methods.
Abstract
Recent works showed that pressure-robust modifications of mixed finite element methods for the Stokes equations outperform their standard versions in many cases. This is achieved by divergence-free reconstruction operators and results in pressure independent velocity error estimates which are robust with respect to small viscosities. In this paper we develop a posteriori error control which reflects this robustness. The main difficulty lies in the volume contribution of the standard residual-based approach that includes the -norm of the right-hand side. However, the velocity is only steered by the divergence-free part of this source term. An efficient error estimator must approximate this divergence-free part in a proper manner, otherwise it can be dominated by the pressure error. To overcome this difficulty a novel approach is suggested that uses arguments from the stream…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Numerical Methods in Computational Mathematics · Computational Fluid Dynamics and Aerodynamics · Model Reduction and Neural Networks
