Physics-informed neural network for open channel flow velocity prediction
Bibliographic record
Abstract
This study presents a physics-informed neural network (PINN) framework for predicting open channel flow velocity, integrating traditional hydraulic principles with modern deep learning techniques when limited data are available. The framework combines a multi-branch neural architecture with a composite loss function that enforces physical consistency through established hydrological principles, including the continuity condition, location-dependent Manning's equation, and geometric constraints. Unlike conventional PINN implementations that require dense spatiotemporal discretization, this approach is specifically designed for sparse data conditions (or local data), utilizing a surrogate model that assimilates limited sensor-based measurements while embedding key physical–empirical laws. The model achieves superior predictive performance compared to traditional machine learning baselines, demonstrating a 40% reduction in mean absolute percentage error relative to standard approaches. Moreover, the framework maintains physical consistency, predicting location-dependent Manning coefficients that align with established hydraulic engineering literature and achieving accurate hydraulic radius predictions across diverse channel configurations. These results suggest that integrating physical constraints effectively compensates for data sparsity, offering a promising solution for applications such as hydrokinetic power assessment where both accuracy and physical plausibility are essential.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".