A New Subspace Optimization Method for Aero-Structural Design
Why this work is in the frame
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Bibliographic record
Abstract
A new subspace optimization method for performing aero-structural design is introduced. The method relies on a semi-analytic approach to the sensitivity analysis that includes post-optimality sensitivity information from the structural optimization subproblem. The resulting coupled post-optimality sensitivity (CPOS) approach is used to guide a gradient-based optimization algorithm. The new approach simplifies the system-level problem, thereby reducing the number of calls to the costly aerodynamics solver. The aero-structural optimization of an aircraft wing is carried out, and it is shown that the proposed method results in a problem equivalent to the conventional approach. The new method is also shown to reduce both the computational time required by the aerodynamic discipline, and the total time required by higher-fidelity optimizations. I.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 it