Sur la résolution efficace d'équations aux dérivées partielles en mécanique des fluides multiphasique et imagerie médicale
Bibliographic record
Abstract
This work is divided into four parts. The first three parts, as a common base, aim at adapting a finite volume scheme (VFFC) to various situations, in order to get a better efficiency for the numerical simulation of complex flows. The first part deals with the efficient numerical simulation of a falling block of liquid in a gas pocket, and proposes a new model to combine previous work for associating precision of results and computational efficiency. The second part aims at the establishment of a general AMR (Adaptive Mesh Refinement) scheme for resolution by finite volumes of non-conservative systems. The purpose of the third part is the dynamic coupling of two models representing more or less finely a given physical system. Finally, in any other area where the efficiency of solving partial differential equations is of great importance too, the fourth part deals with the problem of optical flow in imaging - i.e. the research of a displacement field from several successive images - and deepens an existing method (Horn and Schunck method) from a practical and theoretical perspective.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| 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".