Comparison of Three Surrogate Modeling Techniques: Datascape, Kriging, and Second Order Regression
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
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.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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