Visual HDMR Model Refinement Through Iterative Interaction
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| 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