Comparison of Three Surrogate Modeling Techniques: Datascape, Kriging, and Second Order Regression
Bibliographic record
Abstract
Using surrogate models in place of high fldelity engineering simulations can help reduce design cycle times and cost by enabling rapid analysis of alternative designs. Surrogate models can also be used in a deliverable product as an e‐cient replacement for large lookup tables or as a soft sensor to predict quantities than cannot be directly measured. Many difierent surrogate modeling techniques exist, including new commercial technologies, each with difierent capabilities and pitfalls. The goal of this research is to aid the designer in selecting the appropriate surrogate model by comparing two popular techniques, second order regression and kriging, along with a new commercial application called Datascape. The three difierent modeling techniques are compared on model accuracy, computational e‐ciency, robustness, transparency, and ease of use. The comparisons were done using three test problems: an Earth-Mars transfer orbit problem, the analytic Shekel function, and a low Earth orbit three-satellite constellation design problem. It was found that kriging models performed the best when the sample data used to build the models was sparse, when larger sample sets were used Datascape produced more accurate models.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".