Bibliographic record
Abstract
Delaunay refinement has been established as an effective and efficient technique to generate high quality meshes for arbitrary domains. Starting from a constrained Delaunay triangulation of the input of points and line segments, Delaunay refinement adds carefully chosen Steiner points to the mesh while maintaining the constrained Delaunay property. This strategy results in theoretically sound algorithms that generate quality meshes of optimal small size. Sequential implementations of such algorithms are available and work very well in practice. As the meshing problem is time and memory intense large-scale problems call for the use of parallel computers. This thesis presents a parallel Delaunay refinement algorithm to solve the two-dimensional meshing problem on distributed memory computers. In contrast to other work, no assumptions on the size or the regularity of the input are made. Parallelizing Delaunay refinement is difficult as the underlying theory is intrinsically sequential. To approve a Steiner point for insertion a consistent view of the current state of the mesh in the neighborhood of the new point is needed. Inserting a Steiner point changes the mesh only locally but in an unstructured way, which makes it hard to maintain a partition of the mesh based upon mesh entities. The presented algorithm partitions the mesh based upon certain mesh properties and maintains this partition while the mesh is generated and refined. At first, the input is distributed over the parallel machine by means of projection-based dividers. Then local meshes are generated and refined in parallel. Properties of the projection-based dividers are exploited to achieve a very high level of asynchronicity. This thesis first reviews sequential Delaunay refinement algorithms and extends them to work on constrained Delaunay meshes. In the second part the parallel Delaunay refinement algorithm is introduced and described in detail. Correctness and optimality of the new algorithm are proved before illustrative examples are given.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.008 |
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".