Metamodelling and search using space exploration and unimodal region elimination for design optimization
Bibliographic record
Abstract
Metamodelling based search, space exploration, and region reduction/elimination methods are effective optimization schemes for computation intensive global design optimization problems. In this work a new metamodelling, space exploration and region reduction search algorithm is introduced. This algorithm, namely Space Exploration and Unimodal Region Elimination (SEUMRE), divides the design space into key unimodal regions using design experiment data; identifies the regions that most likely contain the global minimum; fits Kriging models with additional design experiments using Latin Hypercube designs over these regions; identifies their local minima, and then the global optimum. By identifying promising unimodal regions of the objective and reducing searching space, the method can find the global optimum effectively and efficiently, particularly suited for optimization problems that require extensive computation through engineering analyses and simulations. Comparisons with existing space exploration and region elimination/reduction methods using benchmark test problems have been carried out to demonstrate the advantages of the new method. More robust and problem independent metamodelling improvements are under study.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".