Tool-box for parallel adaptive computations of 3-D convection-diffusion problems using domain decomposition
Bibliographic record
Abstract
In this report we describe the tools that we have used, developed, and implemented in a computer system for simulation of flows in porous media. Our goal was to create a simulator that uses various tools and that is based on discretization by finite elements and finite volumes and uses e#cient preconditioning iterative methods for the resulting large sparse system. Also important features are error control, adaptive grid refinement and parallel implementation on multiprocessor computer systems utilizing the concept of domain decomposition. The tools include (1) 3-D mesh generator (NETGEN), (2) partitioning and load balancing software (METIS), (3) local error control and refinement procedures, (4) preconditioning methods based on domain decomposition and multigrid/multilevel, and (5) MPI and the OpenMP standards for massively parallel computations .
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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 | 0.000 | 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) | 0.000 | 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".