Multilevel Monte Carlo applied for uncertainty quantification in stochastic multiscale systems
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The aim of this study is to evaluate the performance of multilevel Monte Carlo (MLMC) sampling technique for uncertainty quantification in stochastic multiscale systems. Two systems, a chemical vapor deposition chamber and a catalytic flow reactor, subject to multiple parameter uncertainty, were considered. The distributions of the systems' observables were estimated using standard MC sampling and polynomial chaos expansions (PCE), where the coefficients were calculated by nonintrusive spectral projection. The MLMC technique was used to efficiently sample the two systems and accurately estimate the data necessary for constructing the PCE expressions. The results show that the usage of MLMC improved the precision of identification of PCE versus the traditional heuristic approach and lowered the computational cost of uncertainty quantification compared to standard MC.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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