Goal-Oriented Adaptive Sampling Procedure for Projection-Based Reduced-Order Models for Aerodynamic Flows
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Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-1564.vid The process of designing modern aircraft involves many design variables from various disciplines. Obtaining high-fidelity solutions for all parameter combinations is computationally unfeasible. As a result, reduced-order models have been a subject of interest as they allow for computing high-fidelity solutions rapidly. However, reduced-order models can introduce errors in the solution, and quantifying this error is critical. When the error is too high for the desired purpose, additional full-order samples must be computed, which is time-consuming and defeats the purpose of the reduced-order model. Therefore, an a priori snapshot sampling that would satisfy a desired error tolerance is preferable. To this end, an adaptive sampling procedure that aims to bring the output error in a projection-based reduced-order model to within a prescribed error tolerance has previously been developed. This work aims to enhance the previous method and demonstrate it on aerodynamic test cases.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it