Design Optimization on "white-box" Uncovered by Metamodeling
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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