Approach for uncertainty propagation and design in Saint Venant equations via automatic sensitive derivatives applied to Saar river
Bibliographic record
Abstract
This paper describes the assessment of uncertainties of computational fluid dynamics (CFD) for modelling free surface flows. A series of CFD simulations, using MAillé GEnéralisé (MAGE), are employed to compute the flood extent resulting from the overflow of rivers. These simulated outputs are affected by uncertainties in the empiric roughness coefficients. Uncertainty propagation in MAGE outputs is difficult to evaluate because of the complexity and the nonlinearity of models. Assessment of uncertainties may be carried out by computing derivatives of the output results with respect to the inputs. Recently, automatic differentiation (AD) has become an efficient numerical method for sensitivity analysis and assessment of uncertainties. In this paper, AD is used to transform mechanically a given one-dimensional hydraulic model, MAGE, into a new program capable of computing the original simulation and the desired derivatives. Specifically, derivatives of the flood extent and the water width with respect to the roughness coefficients are computed. Numerical experiments of derivatives obtained from AD and divided difference (DD) approximations are compared, validating derivatives obtained by AD. Results can serve to evaluate existing flood models.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".