A Filtering Physics-Informed Neural Operator for Online Hydraulic Transient Teconstruction
Bibliographic record
Abstract
Hydraulic transients pose significant risks to pipeline systems, including potential leaks and bursts. While traditional numerical methods are widely used for transient analysis, their reliance on detailed initial/boundary conditions limits their applicability in real-world scenarios. To overcome these challenges, this paper proposes a novel Filtering Physics-Informed Neural Operator (FPINO) framework for accurate and near real-time reconstruction of hydraulic transients from sparse observations. FPINO extends recent developments in Physics-Informed Neural Operators (PINOs), a class of machine learning models that incorporate physical laws directly into operator learning, to transient hydraulic applications. The FPINO framework introduces a time-weighted information filtering strategy that prioritizes the relevant observations, thereby accelerating convergence and improving prediction accuracy. Additionally, FPINO employs a hybrid loss function that integrates physics-based constraints with data-driven learning, ensuring both physical fidelity and adaptability to complex system behaviors. Numerical and experimental results show that FPINO outperforms the baseline PINO approach in reconstructing diverse transient scenarios. These results highlight the potential of FPINO as a robust and practical tool for real-time monitoring and analysis of hydraulic transients in pipeline 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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".