An Algebraic Preconditioner for the Exactly Divergence‐Free Discontinuous Galerkin Method for Stokes
Bibliographic record
Abstract
ABSTRACT We present an algebraic preconditioner for the exactly divergence‐free discontinuous Galerkin (DG) discretization of Cockburn, Kanschat, and Schötzau [J. Sci. Comput., 31 (2007), pp. 61–73] and Wang and Ye [SIAM J. Numer. Anal., 45 (2007), pp. 1269–1286] for the Stokes problem. The exactly divergence‐free DG method uses finite elements that use an ‐conforming basis, thereby significantly complicating its solution by iterative methods. Several preconditioners for this Stokes discretization has been developed, but each is based on specialized solvers or decompositions. To avoid requiring custom solvers, we hybridize the ‐conforming finite element so that the velocity lives in a standard ‐DG space, and present a simple algebraic preconditioner for the extended hybridized system. The proposed preconditioner is optimal in mesh size , effective in 2d and 3d, and only relies on standard relaxation and algebraic multigrid methods available in many packages. Furthermore, the Schur complement approximation is robust in element order , although more AMG cycles are needed on the velocity block when increasing .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".