Modelling and parameter identification of penicillin fermentation using physics‐informed neural networks
Bibliographic record
Abstract
Abstract With the rapid development of machine learning technology and computer science, artificial neural networks have become an effective and popular method in the existing modelling research of penicillin fermentation process. Although these networks can capture the complexity of the fermentation process, they may lead to overfitting and require large amounts of data. In addition, the inference of the model on the data may not satisfy the physical laws. In this paper, a penicillin fermentation modelling method based on physics‐informed neural networks is proposed. The fermentation mechanism equations are combined with the neural networks to develop the model as constraints. First, a general penicillin fermentation mechanism model is built according to known prior knowledge, and then its unknown nonlinear dynamic parameters are identified by physics‐informed neural networks. Finally, the successfully trained model exhibits a high prediction accuracy, which not only satisfies the physical laws in the loss function, but also verifies the effectiveness of the proposed mechanism model.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".