Metric Systems for Performance Evaluation of Active Learning Kriging Configurations for Reliability Analysis
Bibliographic record
Abstract
In active learning Kriging (AK) reliability-based analysis, a surrogate model is trained in a stepwise manner and used to evaluate the reliability of the desired system by reducing the computational cost of analysis. While extensive studies were conducted on advancing the AK reliability methods by developing new learning functions, limited work studied the effect of AK configuration on the accuracy, efficiency, and consistency of the AK reliability analysis. AK configuration is defined herein as a unique set of Kriging correlation, Kriging regression, learning function, and AK reliability method for the AK procedure. This paper presents six metric systems to evaluate the performance of AK reliability analysis based on AK configurations including the comprehensive metric system (CMS), the weighted metric system (WMS) with local optimized weights or average optimized weights (LOW or AOW), and modified desirability function, and two original desirability functions used for multiple response optimization. The ranking optimizes four scaled indexes as measures of accuracy, efficiency, and consistency of the reliability analysis. The metrics are developed and applied to four diverse examples, where a total of 14,400 AK reliability analyses were considered. The results show the validity of the metric systems to rank AK configurations based on their performance.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".