An extension to design of experiment for design optimization with implicit parametric models and virtual prototypes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Virtual prototyping is a new technique used by leading industry to develop new products without numerous costly prototypes. Variations to the virtual computer model can be made to identify the best overall design before the production of a few physical prototypes to validate the computation results. In this work, an adaptive response surface method (ARSM) is introduced to satisfy the needs of complex optimal designs driven by virtual prototyping. The method extends the current one-time RSM with better search efficiency and more accurate results in the design optimization of a complex problem. The adaptive response surface method applies nonlinear approximation to unknown functions, gradually improves the approximation model, and approaches the design optimum. A two-bar design example is used to demonstrate the newly introduced method.
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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 | 0.000 |
| 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 | 0.000 | 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