A Hybrid Relationship Modeling Scheme for Parametric Design Considering Uncertainties
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Bibliographic record
Abstract
This research introduces a new scheme to model different types of relationships in parametric design considering uncertainties. First a hybrid parameter relationship network is developed to associate the parameters through their relationships. In this hybrid parameter relationship network, in addition to the deterministic parameters and relationships, non-deterministic parameters (e.g., random parameters and fuzzy parameters) and non-deterministic relationships (e.g., neural network relationships and fuzzy relationships) can also be modeled. Propagation of parameter values and their uncertainties through this hybrid parameter relationship network is then investigated. Two optimization mechanisms, probability based design optimization and possibility based design optimization, are employed to identify the optimal design considering objective random uncertainties and subjective fuzzy uncertainties. A computer tool has been implemented and used for the optimal design of a solid oxide fuel cell (SOFC) system.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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