An efficient method for calculating the moments of multidimensional growth processes in population balance systems
Bibliographic record
Abstract
Abstract Multidimensional growth processes play an important role in many fields of applications, such as crystallization processes and cell culture engin‐ eering. The numerical solution of the corresponding multivariate population balance equations is quite challenging as standard discretization‐based methods are not efficient for high dimensional problems. For this reason, the distribution dynamics is often characterized by its moments. However, the moment dynamics generally cannot be calculated in closed form. Existing approximation techniques cannot be implemented efficiently in the multivariate framework, in particular for high dimensional problems. This contribution presents an alternative methodology for the efficient approximate computation of moments for multidimensional growth processes using Monomial Cubatures and the Method of Characteristics (MOC). The procedure will be shown to reproduce the moments accurately for one‐ and two‐dimensional examples which feature nonlinear growth rates and coupling to a continuous phase, respectively. Furthermore numerical effort and accuracy will be analyzed for a five‐dimensional benchmark problem.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".