Design Optimization on "white-box" Uncovered by Metamodeling
Bibliographic record
Abstract
In the area of Multidisciplinary Design Optimization (MDO), a majority of problems involve so called high-dimensional, expensive, black-box (HEB) functions, such as complex finite element analyses or computational fluid dynamics simulations. A new metamodeling approach, the radial-basis function-high dimensional model representation (RBF-HDMR) method, was recently developed for HEB problems. RBF-HDMR adaptively models a HEB problem according to the problem’s intrinsic (non)linearity, variable correlations, and variable structures. Therefore in a sense it is able to turn a “black-box” function in a “white-box.” This work explores the application of RBF-HDMR in the context of optimization. The model is first applied to uncover the variable structure and correlations, based on which the HEB problem is then decomposed to sub-problems. Optimization is then applied to those sub-problems. This simple strategy is then compared with direct optimization without decomposition. From the tests, the pros and cons of the strategy will be discussed.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.002 |
| 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".