A posteriori error estimation using a Bank-Weiser type estimator
Definition of the estimator
Let us call true error the difference between the solution of our PDE and its Galerkin approximation. We build the Bank-Weiser estimator (BW estimator) from a specific discontinuous polynomial function called residual solution. This function is defined locally on each cell of the mesh as the elliptic projection (in the sense of Céa’s lemma, see ) of the (local) true error on a specific discontinuous finite elements (FE) space.
This FE space is chosen in order to ensure that this residual solution is well-defined. Indeed, the computation of this residual solution implies to solve local Neumann problems on each cell and specific assumptions must be made to ensure their well-posedness.
Since this solution is defined as a projection, it is the best approximation of the true error we can find on this subspace. Finally, we define the BW estimator as the sum over the cells of the norm of the energy of the residual solution.
Cons and pros of the Bank-Weiser estimator
The first advantage of the BW estimator is its accuracy for problems admitting a rough solution (H1) as well as when the solution is more regular (H2). Indeed, numerical results has been done in the case H1 in  when the solution admits a singularity. Similarily, the estimator has been generalized to the Stokes problem in  and the numerical results suggest a good accuracy in this case as well.
Another advantage of the BW estimator is the (relative) rapidity of computation, due to the choice of the FE space containing the residual solution.
However, the main disadvantage of the BW estimator lays in the lack of theoretical guaranties. Indeed, the proof of the equivalence between this estimator and the true error has been proved (in ) but only under a constraining
assumption called saturation assumption. This assumption can be related to the regularity of the data (and then the regularity of the solution). It is possible to find very simple cases where the saturation assumption fails, see . Also, the numerical examples shown in [3, 5] do not satisfy this assumption but the BW estimator remains accurate.
For the time being the theory can not fully explain this accuracy except in the case of linear FE with a proof from R. H. Nochetto in . Even if the numerical results seem to suggest it, the extension of this proof to higher order FE is still an open question.
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 – R. E. Bank and B. D. Welfert. A posteriori error estimates for the Stokes problem. SIAM J. Numer. Anal.591–623, 1991.
 – W. Dörfler and R. H. Nochetto. Small data oscillation implies the saturation assumption. Numer. Math. 1–12, 2002.
 – R. H. Nochetto. Removing the saturation assumption in a posteriori error analysis. Istit. Lombardo Accad. Sci. Lett. Rend. A. 67–82, 1993.