Improvement on the Adaptive Response Surface Method for High-Dimensional Computation-Intensive Design Problems
Why this work is in the frame
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Bibliographic record
Abstract
Abstract This paper addresses the difficulty of the previously developed Adaptive Response Surface Method (ARSM) for high-dimensional design problems. The ARSM was developed to search for the global design optimum for computation-intensive design problems. This method utilized the Central Composite Designs (CCD), which resulted in an exponentially increasing number of required design experiments. In addition, the ARSM generates a complete new set of CCDs in a gradually reduced design space. These two factors greatly undermine the efficiency of the ARSM. In this work, the Latin Hypercube Designs (LHD) were utilized to generate saturated design experiments. Because of the use of Latin Hypercube Designs, the historical design experiments can be inherited in later iterations. The improved ARSM has been tested using a group of standard testing problems and then applied to an engineering design. In both testing and design application, significant efficiency improvement of the ARSM was observed. The ARSM at the current stage demonstrated strong potential to be an efficient global optimization tool for computation-intensive design problems.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| 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