Physics Informed Neural Network model for the dynamical study of Abdominal Aortic Aneurysm

2026-09-14

AI Summary

研究使用三维物理信息神经网络模型来研究腹主动脉瘤的血流动力学行为，通过自动微分能力避免了传统计算流体动力学方法中的网格生成问题，提高了计算效率。

Abstract

We present the development and application of a three-dimensional Physics-Informed Neural Network (PINN) framework for the investigation of haemodynamic behaviour in the human aorta. The model incorporates a time-resolved simulation of pulsatile blood flow over a two-minute interval, enabling the extraction of pressure and velocity fields with high temporal fidelity. The mechanical stress exerted on the aortic wall was quantified through Laplace's law, with temporal averaging applied to derive representative stress distributions. This approach circumvents the computational overhead associated with conventional computational fluid dynamics (CFD) methods by eliminating mesh generation and exploiting the automatic differentiation capabilities inherent to neural networks. The proposed methodology demonstrates that PINNs can serve as an efficient and accurate alternative for modelling complex vascular flow phenomena, offering significant advantages in scalability and computational cost reduction while maintaining physical consistency.

Problem

• Physics-Informed Neural Network
• haemodynamic behaviour
• Abdominal Aortic Aneurysm
• computational fluid dynamics

Innovation

• Physics-Informed Neural Network (PINN)
• hemodynamic behavior
• computational fluid dynamics (CFD)
• automatic differentiation
• aortic wall stress