Multilevel Monte Carlo applied to chemical engineering systems subject to uncertainty
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 aim of this study is to evaluate the performance of Multilevel Monte Carlo (MLMC) sampling technique for uncertainty quantification in chemical engineering systems. Three systems (a mixing tank, a wastewater treatment plant, and a ternary distillation column, all subject to uncertainty) were considered. The expected values of the systems' observables were estimated using MLMC, Power Series and Polynomial Chaos expansions, and standard Monte Carlo (MC) sampling. The MLMC technique achieved results of significantly greater accuracy than other methods at a lower computational cost than standard MC. This study highlights the nuances of adapting the MLMC technique to chemical engineering systems and the advantages of using MLMC for uncertainty quantification. © 2017 American Institute of Chemical Engineers AIChE J , 64: 1651–1661, 2018
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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 | 0.007 |
| 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 | 0.001 | 0.000 |
| Open science | 0.002 | 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