Contribution à l’automatisation des analyses par éléments-finis multidimensionnelles
Bibliographic record
Abstract
La réduction du nombre de degrés de liberté d’une étude par éléments-finis peut être obtenue en utilisant l’analyse par éléments-finis multidimensionnelle, c’est-à-dire le mélange d’éléments-finis de poutre, de coque et de volume. Cette approche multidimensionnelle permet de réduire considérablement le temps de maillage et de résolution du système. Malheureusement, la connexion d’éléments de différentes dimensions entraîne certains problèmes au niveau de la modélisation géométrique ainsi qu’au niveau de l’incompatibilité des degrés de liberté entre éléments et de la continuité entre différentes parties de maillage réalisées séparément. Cet article présente une solution complètement automatisée à ces problèmes, utilisant uniquement des éléments-finis classiques et sans recourir à l’ajout d’équations de contraintes aux interfaces entre éléments de dimensions différentes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".