Physics-Informed Neural Networks for Process Systems: Handling Plant-Model Mismatch
Bibliographic record
Abstract
This work addresses the problem of leveraging first-principles knowledge with data-driven techniques in the Physics-Informed/Inspired Neural Network (PINN) framework to handle plant–model mismatch. To this end, a PINN is developed utilizing the first-principles model of the system and plant data and demonstrated to handle plant–model mismatch. The PINN is compared with another dynamic modeling technique, a Recurrent Neural Network (RNN), and for the illustrative simulation example, is shown to improve the predictive capabilities of the model compared to the other techniques. In particular, purely data-driven approaches often encounter challenges when applied to complex systems. This can lead to compromised predictive performance in situations where the model fails to capture the actual relationships among system variables. In contrast, the PINN respects the physical characteristics of the problem, while yielding a good dynamic model, based on process data. These results indicate the benefit of utilizing hybrid modeling techniques and their potential application to more complex systems.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".