Optimal Design for Uncertain Load Cases Using Convex Hulls
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Bibliographic record
Abstract
The optimal design of a structure subjected to uncertain loads is considered. Convex modeling, a deterministic approach for optimal design of a structure subjected to uncertain load cases, is used. Two approaches to design are considered. First, each load is assumed to vary continuously between a lower and upper limit and an optimal design is obtained. Second, discrete load cases lying between the limits are identied and the structure is designed for all possible cases. It is found that in the rst approach, the structure is over-designed as compared to the second approach. Constraints such as stress and deection, which are explicitly dependent on loads, are used. The number of such constraints multiplies with an increase in the number of discrete load cases. Computational expense and optimizer convergence problems increase signican tly with an increasing number of constraints. A convex hull approach is used to reduce the number of discrete load cases in order to reduce computational expense and to reduce convergence problems. A ten-bar truss is used to demonstrate the proposed approach.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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