Abstract Parallel Unstructured Mesh Generation by an Advancing Front Method
Bibliographic record
Abstract
Mesh generation is a critical step in high fidelity computational simulations. High-quality and high-density meshes are required to accurately capture the complex physical phenomena. A robust approach for a parallel framework has been developed to generate large-scale meshes in a short period of time. A coarse tetrahedral mesh is generated first to provide the basis of block interfaces and then is partitioned into a number of sub-domains using METIS partitioning algorithms. A volume mesh is generated on each sub-domain in parallel using an advancing front method. Dynamic load balancing is achieved by evenly distributing work among the processors. All the sub-domains are combined to create a single volume mesh. The combined volume mesh can be smoothed to remove the artifacts in the interfaces between sub-domains. A void region is defined inside each sub-domain to reduce the data points during the smoothing operation. The scalability of the parallel mesh generation is evaluated to quantify the improvement on shared- and distributed-memory computer systems. KEY WORDS: unstructured mesh generation; parallel; MPI; advancing front method 1
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".