Fuzzy Modeling in Response Surface Method for Complex Computer Model Based Design Optimization
Why this work is in the frame
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Bibliographic record
Abstract
Metamodel based optimization serves as an effective tool for carrying out multi-disciplinary and multi-objective design optimization using complex, "black box" computer modeling, analysis and simulation tools. The response surface based metamodeling method uses simple surrogate polynomial model to approximate the complex objective and constraint functions to reduce computation time, thus making prohibitive global optimization requiring extensive computation feasible. In this paper, another important issue in metamodeling, the uncertainty of the result data from the "black box" functions and their appropriate processing are addressed. The newly introduced fuzzy modeling method inherits the advantages of the well tested response surface method and removes a major fault assumption of the approach on sure result data, thus leading to better accuracy to the identified design optimum. The close form solution of the second order response surface model in fuzzy setting has been derived and demonstrated using a bench mark global design optimization problem
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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