Unconventional DDM & parallel method for fluid computation (Computation mechanics and domain decomposition methods)
Bibliographic record
Abstract
Unconventional DDM¶llel method for fluid computation Shi Dongyang $(\mathrm{T}\mathrm{l}\mathrm{T}\prime \mathrm{z}\mathrm{z}\mathrm{U})$ Wu Yuchun $(\mathrm{T}\mathrm{l}\mathrm{T}/\mathrm{H}\mathrm{l}\mathrm{T})$ Ichiro Hagiwara $(\mathrm{T}\mathrm{l}\mathrm{T})$ A new finite element Domain decomposition rnethod, which is bascd on a $\mathrm{p}\mathrm{o}\mathrm{i}\mathrm{n}\iota$ -by-point scheme.domain decomposition method (DDM) and the matrix-storage frce formulation.is developed and implemented to the model equation for Navicr-Stokes equations, convection-diffusion equation.Numerical experiments demonstrated that the proposed method is efficiently to solvc the modcl $\mathrm{e}\mathrm{q}\mathrm{u}\mathrm{a}\mathrm{l}\mathrm{i}_{0}\iota 1$ ' Key words: Domain Decomposition Method, Locally lmplicit Finite $\mathrm{E}\mathrm{l}\mathrm{e}\mathrm{m}\mathrm{e}\ulcorner \mathrm{l}\mathrm{t}$ Scheme.Point by Point, Stiffncss Matrix $\mathrm{f}_{t\mathrm{C}\mathrm{C}},$ $\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{v}\mathrm{e}\mathrm{C}(\mathrm{i}\mathrm{o}\mathrm{n}\cdot \mathrm{D}\mathrm{i}i\mathrm{f}_{\mathrm{U}}\mathrm{s}\mathrm{i}\mathrm{o}\mathrm{n}$ Equation
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".