MODELING STATISTICAL UNCERTAINTIES IN ROBUST ENGINEERING DESIGN
Bibliographic record
Abstract
Robust design deals with the goal of achieving a targeted high performance when the system is subjected to exterior disturbances or fluctuations. Although several robust design schemes have been proposed by different researchers, they have all omitted the effects of statistically induced uncertainties associated with empirical design parameters (such as material properties and applied loads), on the design’s performance. This work aims at developing an adaptive robust design approach for handling evolutionary statistical uncertainties present in the problem formulation via Bayesian statistics. By realizing the statistical nature of design parameters (i.e. the more experimental data collected. The closer the parameter estimates will be to their true values), the proposed robust design scheme allows a designer to adjust and update the empirical parameters continuously during the design progression, provided that new or additional (experimental) data are generated, measured, and collected in a prescribed sequence. A welded beam design problem is considered to illustrate the proposed approach, in which a Monte Carlo technique is employed to simulate the statistical feature of the (empirical) design parameters.
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How this classification was reachedexpand
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 | 0.002 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".