From: Martin Kronbichler <kronbichler@lnm.mw.tum.de>
Date: Tue, 30 Jul 2019 17:17:04 +0000 (+0200)
Subject: Merge pull request #8416 from bangerth/z
X-Git-Tag: v9.2.0-rc1~1340
X-Git-Url: https://gitweb.dealii.org/cgi-bin/gitweb.cgi?a=commitdiff_plain;h=f4acfa573a6fd07d0b150766b759cc86d4da1fde;p=dealii.git

Merge pull request #8416 from bangerth/z

Update some commentary.
---

f4acfa573a6fd07d0b150766b759cc86d4da1fde
diff --cc include/deal.II/hp/refinement.h
index 3f9893906a,d9aa1522d2..3ef7c9145d
--- a/include/deal.II/hp/refinement.h
+++ b/include/deal.II/hp/refinement.h
@@@ -294,30 -293,46 +294,46 @@@ namespace h
       * results to @p predicted_errors. Each entry of @p error_indicators and
       * @p predicted_errors corresponds to an active cell on the underlying
       * Triangulation, thus each container has to be of size
-      * Triangulation::n_active_cells().
+      * Triangulation::n_active_cells(). The errors are interpreted to be
+      * measured in the energy norm; this assumption enters the rate of
+      * convergence that is used in the prediction. The `predicted_errors` output
+      * argument has one entry per <i>current</i> cell, with the $2^d$ values for
+      * each cell that will be coarsened away equal, and with the value stored on
+      * a cell to be refined interpreted as applying to each of the future
+      * children.
       *
 -     * For h adaptation, we expect the local error $\eta_K$ on cell $K$ to be
 +     * For h-adaptation, we expect the local error $\eta_K$ on cell $K$ to be
       * proportional to $(h_K)^{p_K}$ in the energy norm, where $h_K$ denotes the
       * cell diameter and $p_K$ the polynomial degree of the currently assigned
-      * finite element. Here, we assume that the finite element will not change
-      * in the adaptation process so that $p_K = \text{const}$. However during
-      * coarsening, the finite elements on siblings may be different, and their
-      * parent cell will be assigned to their least dominating finite element
-      * that belongs to its most general child. Thus, we will always interpolate
-      * on an enclosing finite element space. Additionaly assuming that the
-      * finite elements on the cells to be coarsened are sufficient to represent
-      * the solution correct (e.g. at least quadratic basis functions for a
-      * quadratic solution), we are confident to say that the error will not
-      * change by sole interpolation on the larger finite element space.
+      * finite element on cell $K$. Here, we assume that the finite element will
+      * not change in the adaptation process so that $p_K = \text{const}$.
+      * However during coarsening, the finite elements on siblings may be
+      * different, and their parent cell will be assigned to their least
+      * dominating finite element that belongs to its most general child. Thus,
+      * we will always interpolate on an enclosing finite element space.
+      * Additionaly assuming that the finite elements on the cells to be
+      * coarsened are sufficient to represent the solution correctly (e.g. at
+      * least quadratic basis functions for a quadratic solution), we are
+      * confident to say that the error will not change by sole interpolation on
+      * the larger finite element space.
       *
-      * Further, we expect that the local error will be divided equally on
-      * all $2^{dim}$ children during refinement, whereas local errors on
-      * siblings will be summed up on the parent cell in case of coarsening. When
-      * transferring the predicted error to the coarsened mesh, make sure to
-      * configure your CellDataTransfer object with
-      * GridTools::CoarseningStrategies::sum() as a coarsening strategy.
+      * Further, the function assumes that the local error on a cell
+      * that will be refined, will lead to errors on the $2^{dim}$
+      * children that are all equal, whereas local errors on siblings
+      * will be summed up on the parent cell in case of
+      * coarsening. This assumption is often not satisfied in practice:
+      * For example, if a cell is at a corner singularity, then the one
+      * child cell that ends up closest to the singularity will inherit
+      * the majority of the remaining error -- but this function can
+      * not know where the singularity will be, and consequently
+      * assumes equal distribution.
+      *
+      * When transferring the predicted error to the coarsened mesh,
+      * make sure to configure your CellDataTransfer object with
+      * CoarseningStrategies::sum() as a coarsening
+      * strategy.
       *
 -     * For p adaptation, the local error is expected to converge exponentially
 +     * For p-adaptation, the local error is expected to converge exponentially
       * with the polynomial degree of the assigned finite element. Each increase
       * or decrease of the degree will thus change its value by a user-defined
       * control parameter @p gamma_p. The assumption of exponential convergence