Adaptive Gaussian Process Surrogate Models for Efficient Uncertainty Quantification of the Flutter Boundary
Bibliographic record
Abstract
We present and assess sequential design methods that construct surrogate models to estimate the probability of flutter in the presence of model uncertainties for a range of flight conditions. Our goal is to provide a probabilistic characterization of the flutter boundary that accounts for uncertainties in model parameters and operating conditions to better inform design and testing decisions in the certification of new aircraft. We use adaptively constructed Gaussian process surrogate models and high-fidelity computational aeroelasticity models to efficiently characterize sensitive features like the transonic dip while controlling the cost of many-query uncertainty analysis. To obtain acquisition functions for adaptive sampling, we derive relationships between misclassification of flutter risk and existing sequential design strategies, propose a new sampling strategy, and use the strategies to accurately approximate flutter probability within a limited sampling budget. We assess the efficiency of these methods using synthetic test functions, a traditional aeroelasticity model with Theodorsen's aerodynamics model, and a computational aeroelasticity model based on the Euler equations.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".