Panel discussion: integrating data from multiple simulation models of different fidelity
Bibliographic record
Abstract
Computer models are used to simulate physical processes in almost all areas of science and engineering. A single evaluation of these computation models (or computer codes) can take as little as a few seconds or as long as weeks or months. In either case, experimenters use the model outputs to learn something about the physical system. In some settings, outputs from several computational models, with varying levels of fidelity, are available to researchers. In addition, observations from the physical system may also be in hand. In this panel discussion we address issues relating to model formulation, estimation, prediction and extrapolation using multi-fidelity computer models are addressed. In the first presentation, Bayesian methods are used to build a predictive model using low and high fidelity computational models with different inputs and also field observations. The second presentation deals with the difficult computational issues facing computer model calibration and prediction using a Bayesian framework that are typically remedied through the use of Markov Chain Monte Carlo techniques. While the computational burden is substantial, we review faster alternatives to standard MCMC techniques that are particularly useful in the multi-fidelity simulator problem. In the final presentation, calibration of computational models is discussed in the context of validation and extrapolation, with introduction to developments in stochastic model calibration.
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.015 | 0.013 |
| Insufficient payload (model declined to judge) | 0.063 | 0.020 |
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".