物理信息神经网络模型用于腹主动脉瘤的动力学研究
Physics Informed Neural Network model for the dynamical study of Abdominal Aortic Aneurysm
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中文总结 AI 辅助
本研究提出三维物理信息神经网络（PINN）框架，模拟腹主动脉瘤血流动力学，通过拉普拉斯定律计算壁面应力，避免了传统CFD的网格生成，实现了高效准确且可扩展的血管流动建模。
中文摘要 AI 辅助
我们提出了一个三维物理信息神经网络（PINN）框架的开发与应用，用于研究人体主动脉中的血流动力学行为。该模型包含对两分钟间隔内脉动血流的时变模拟，从而能够以高时间保真度提取压力和速度场。通过拉普拉斯定律量化了施加在主动脉壁上的机械应力，并采用时间平均来推导具有代表性的应力分布。该方法通过消除网格生成并利用神经网络固有的自动微分能力，绕过了传统计算流体动力学（CFD）方法相关的计算开销。所提出的方法表明，PINN可以作为建模复杂血管流动现象的高效且准确的替代方案，在可扩展性和计算成本降低方面提供显著优势，同时保持物理一致性。
英文摘要
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.
发表机构
- Lucentia Research（朗讯科技研究院）
- Instituto Universitario de Investigación en Informática（大学计算机研究所）
- Universidad de Alicante（阿利坎特大学）
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