Visual HDMR Model Refinement Through Iterative Interaction
Bibliographic record
Abstract
In engineering design, time-consuming simulations may be needed to find the input-output relationship of a system. High Dimensional Model Representation (HDMR) alleviates the need for intensive simulation by approximating the system’s design space with a surrogate model. Although HDMR can provide an overview, specific regions of interest to the designer may require higher accuracy. This paper presents a tool to visualize and interactively improve HDMR accuracy in specified regions of the design space. Regions of the HDMR are selected by iterative brushing in two-dimensional scatterplot planes. Once a region is chosen, designers may concentrate sampling within its bounds to improve the model locally. Regions can be also improved by modeling the error with a localized radial basis function (RBF) metamodel. The effect of local refinement was further evaluated with localized performance metrics. Testing of the tool shows that it can effectively display and improve HDMR models in regions of interest, if there are variables which have a dominating influence on the output.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".