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Prediction of Blood Flow Field in Artery Stenosis Based on Hard Boundary-Constrained Physics-Informed Neural Network
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Huaxin XIANG, Jianbing SANG, Jingyuan Wang, Mengqiang JI, Chen ZHANG
Journal of Medical Biomechanics | 2025, 40(5) : 1222 - 1229
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Journal of Medical Biomechanics | 2025, 40(5): 1222-1229
Original Articles
Prediction of Blood Flow Field in Artery Stenosis Based on Hard Boundary-Constrained Physics-Informed Neural Network
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Huaxin XIANG, Jianbing SANG, Jingyuan Wang, Mengqiang JI, Chen ZHANG
Affiliations
  • School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China
Published: 2025-10-01 doi: 10.16156/j.1004-7220.2025.05.019
Outline
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Objective

To address the limitations of conventional physics-informed neural network (PINN) in handling hemodynamic boundary constraints, an improved hard boundary-constrained PINN (HBC-PINN) framework was proposed to achieve precise prediction of blood flow fields within stenotic arteries.

Methods

An idealized stenosed vessel geometry model was established and computational fluid dynamic simulation was performed to obtain a validation dataset. Appropriate boundary dependent trial functions were designed according to the hard constraint method to embed the flow boundary conditions into the network output. Thus, an HBC-PINN model with the hard boundary constraint method was constructed to predict the velocity field and pressure field of stenosed blood flow. Meanwhile, an original PINN model with the soft constraint method was also built for comparison. By evaluating the accuracy of the two models on the validation dataset, the capability of the HBC-PINN model to simulate hemodynamics without using any labeled data for training was verified.

Results

The effectiveness of the HBC-PINN method in predicting hemodynamic parameters in stenosed blood flow tasks was validated. The relative L2 errors of the flow velocity and pressure predicted by the HBC-PINN in two different stenosis scenarios were both lower than 0.5%, representing an improvement of over 48.8% in accuracy compared to the original PINN model. Additionally, the prediction accuracy of the transverse velocity also increased by more than 35.4%.

Conclusions

Implementing hard constraints on boundary conditions in the PINN modeling process can effectively improve the prediction accuracy of hemodynamic parameters and the efficiency of model solving.

vascular stenosis  /  hemodynamics  /  physics-informed neural network  /  hard constraint method
Huaxin XIANG, Jianbing SANG, Jingyuan Wang, Mengqiang JI, Chen ZHANG. Prediction of Blood Flow Field in Artery Stenosis Based on Hard Boundary-Constrained Physics-Informed Neural Network[J]. Journal of Medical Biomechanics, 2025 , 40 (5) : 1222 -1229 . DOI: 10.16156/j.1004-7220.2025.05.019
Year 2025 volume 40 Issue 5
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Article Info
doi: 10.16156/j.1004-7220.2025.05.019
  • Receive Date:2025-02-26
  • Online Date:2026-03-27
  • Published:2025-10-01
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History
  • Received:2025-02-26
  • Revised:2025-03-24
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    School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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