Data-Driven Acceleration and Sensitivity Study of an Acoustic Cascade Response Model
Bibliographic record
Abstract
The acoustic cascade response model of Posson et al. [JFM, vol. 663, 2010] is revisited in this work. A sensitivity study on the input parameters is done, identifying regions where the cascade unsteady loading becomes resonant at specific frequencies. Then, the model is computationally benchmarked, finding a relatively high computational cost for the model evaluation related to the calculation of several infinite products of functions arising from the Wiener-Hopf kernel decomposition. To accelerate acoustic predictions, data-driven surrogates (Gaussian Processes and Neural Networks) are trained on datasets of model evaluations. Such data-driven surrogates are shown to approximate well the overall features of the response surface even at the low-data regime. However, capturing the sparse high-amplitude peaks requires a much denser data sampling, increasing the training cost of the surrogates. Finally, the computational cost of the newly developed neural network surrogates is evaluated, showing a significant decrease in the cost of at least five orders of magnitude with respect to the analytical model.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".