Pinning down the accuracy of physics-informed neural networks under laminar and turbulent-like aortic blood flow conditions
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Bibliographic record
Abstract
BACKGROUND: Physics-informed neural networks (PINNs) are increasingly being used to model cardiovascular blood flow. The accuracy of PINNs is dependent on flow complexity and could deteriorate in the presence of highly-dynamical blood flow conditions, but the extent of this relationship is currently unknown. Therefore, we investigated the accuracy and performance of PINNs under a range of blood flow conditions, from laminar to turbulent-like flows. METHODS: ) cases were trained in this study. The PINNs architecture and data have been made open-sourced. RESULTS: PINNs errors increased substantially for stenosis severity >50% (stenotic Reynolds numer > 2000) due to the presence of complex turbulent-like flow features. When using 400 sensor points, PINNs velocity magnitude errors ranged from 30% for no-stenosis model to 57% for the model with 70% stenosis, and dropped to 10% and 20%, respectively when the number of sensor points were increased to 1600. PINNs velocity magnitude errors increased monotonically with turbulent intensity, particularly beyond stenosis severity of 50%. CONCLUSIONS: Our findings indicate that the accuracy of PINNs is dependent on the complexity of blood flow conditions. Using conventional PINNs architecture, the errors in trained velocity can increase substantially in the presence of turbulent-like blood flows that are typically found in various vascular pathologies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it