Orthogonality constrained gradient reconstruction for superconvergent linear functionals
| Autor: | Roberto Porcu, Maurizio M. Chiaramonte |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Discretization
Computer Networks and Communications Applied Mathematics Computation 010103 numerical & computational mathematics Superconvergence 01 natural sciences Finite element method 010101 applied mathematics Computational Mathematics Orthogonality Bounded function Convergence (routing) Applied mathematics 0101 mathematics Galerkin method Software Mathematics |
| Zdroj: | BIT Numerical Mathematics. 60:465-479 |
| ISSN: | 1572-9125 0006-3835 |
| Popis: | The post-processing of the solution of variational problems discretized with Galerkin finite element methods is particularly useful for the computation of quantities of interest. Such quantities are generally expressed as linear functionals of the solution and the error of their approximation is bounded by the error of the solution itself. Several a posteriori recovery procedures have been developed over the years to improve the accuracy of post-processed results. Nonetheless such recovery methods usually deteriorate the convergence properties of linear functionals of the solution and, as a consequence, of the quantities of interest as well. The paper develops an enhanced gradient recovery scheme able to both preserve the good qualities of the recovered gradient and increase the accuracy and the convergence rates of linear functionals of the solution. |
| Databáze: | OpenAIRE |
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