Multiresponse Metamodeling in Simulation-Based Design Applications
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
The optimal design of complex systems in engineering requires the availability of mathematical models of system’s behavior as a function of a set of design variables; such models allow the designer to search for the best solution to the design problem. However, system models (e.g., computational fluid dynamics (CFD) analysis, physical prototypes) are usually time-consuming and expensive to evaluate, and thus unsuited for systematic use during design. Approximate models of system behavior based on limited data, also known as metamodels, allow significant savings by reducing the resources devoted to modeling during the design process. In this work in engineering design based on multiple performance criteria, we propose the use of multi-response Bayesian surrogate models (MR-BSM) to model several aspects of system behavior jointly, instead of modeling each individually. To this end, we formulated a family of multiresponse correlation functions, suitable for prediction of several response variables that are observed simultaneously from the same computer simulation. Using a set of test functions with varying degrees of correlation, we compared the performance of MR-BSM against metamodels built individually for each response. Our results indicate that MR-BSM outperforms individual metamodels in 53% to 75% of the test cases, though the relative performance depends on the sample size, sampling scheme and the actual correlation among the observed response values. In addition, the relative performance of MR-BSM versus individual metamodels was contingent upon the ability to select an appropriate covariance/correlation function for each application, a task for which a modified version of Akaike’s Information Criterion was observed to be inadequate.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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