Arbitrary-order pressure-robust DDR and VEM methods for the Stokes problem on polyhedral meshes
Louren\c{c}o Beir\~ao da Veiga, Franco Dassi, Daniele A. Di Pietro and, J\'er\^ome Droniou

TL;DR
This paper introduces pressure-robust DDR and VEM methods for the Stokes problem on polyhedral meshes, achieving high-order accuracy without submesh conforming, and explores their theoretical relations and properties.
Contribution
The paper develops pressure-robust, high-order DDR and VEM methods for the Stokes equations on polyhedral meshes, avoiding submesh conforming constructions and analyzing their interrelations.
Findings
Pressure-robust error estimates of order h^{k+1} are proven.
Numerical tests confirm theoretical error estimates.
DDR and VEM complexes can be reformulated into each other, satisfying commuting properties.
Abstract
This paper contains two major contributions. First we derive, following the discrete de Rham (DDR) and Virtual Element (VEM) paradigms, pressure-robust methods for the Stokes equations that support arbitrary orders and polyhedral meshes. Unlike other methods presented in the literature, pressure-robustness is achieved here without resorting to an -conforming construction on a submesh, but rather projecting the volumetric force onto the discrete space. The cancellation of the pressure error contribution stems from key commutation properties of the underlying DDR and VEM complexes. The pressure-robust error estimates in (with denoting the meshsize and the polynomial degree of the DDR or VEM complex) are proven theoretically and supported by a panel of three-dimensional numerical tests. The second major…
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 · Matrix Theory and Algorithms · Model Reduction and Neural Networks
