A Comparison of Surrogate Models in the Framework of an MDO Tool for Wing Design
Bibliographic record
Abstract
The replacement of the analysis portion of an optimization problem by its equivalent metamodel usually results in a lower computational cost. In this paper, three different metamodels are compared against the conventional non-approximative approach: quadratic interpolation based response surfaces, Kriging and Artificial Neural Networks (ANN). The results obtained from the solution of three different case studies based on aircraft design problems reinforces the idea that quadratic interpolation is only well suited to very simple problems. At higher dimensionality, the usage of the more the complex Kriging and ANN models may result in considerable performance benefits. Nomenclature b/2 Wing semispan, m c, ci Coefficients for polynomial interpolation cbs Wing breakstation chord, m croot Wing root chord, m ctip Wing tip chord, m f (x) Regression model (Kriging) g (x) Constraint function nDV Number of design variables ns Number of samples nt Number of terms in polynomial interpolation/regression approximation qk(x) Values of regression functions at sample locations (Kriging) R (w,x, θ) Correlation model (Kriging) sk Vector of independent variable samples (Kriging)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".