Improving Multi-Response Metamodels With Upper/Lower Bound Information Using Multi-Stage, Non-Stationary Covariance Functions
Bibliographic record
Abstract
Metamodels have been proposed in the literature to reduce the time and resources devoted to design space exploration, to learn about design trade-offs, and to find the best solution to the design problem in the context of simulation-based design and optimization. In previous work in engineering design based on multiple performance criteria, we have proposed the use of Multi-response Bayesian Surrogate Models (MR-BSM) to model several response variables simultaneously, instead of modeling them independently. By doing so, it is expected that the correlation among the response variables can be used to achieve better models with smaller data sets. In this work, we extend the capabilities of MR-BSM by developing a multistage formulation with non-stationary covariance functions. This formulation for multi-response metamodeling in successive stages of experimental design, data acquisition and model fitting, enables the integration of different sources of information about system responses, with different levels of accuracy, into a single, global model of the system. The feasibility of the proposed formulation is demonstrated with an example in which two test functions are jointly approximated in two stages. In addition, we demonstrate the potential of the methodology to take advantage of a priori information, expressed as upper and lower bounds on the responses, to improve the accuracy of the metamodels. Results show that the use of bound information can result in order-of-magnitude improvements in metamodel accuracy.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".