The complexity of the cardiovascular system needs to be accurately reproduced in order to promptly acknowledge health conditions; to this aim, advanced multifidelity and multiphysics numerical models are crucial. On one side, Full Order Models (FOMs) deliver accurate hemodynamic assessments, but their high computational demands hinder their real-time clinical application. In contrast, ROMs provide more efficient yet accurate solutions, essential for personalized healthcare and timely clinical decision-making. In this work, we explore the application of computational fluid dynamics (CFD) in cardiovascular medicine by integrating FOMs with ROMs for predicting the risk of aortic aneurysm growth and rupture. Wall Shear Stress (WSS) and the Oscillatory Shear Index (OSI), sampled at different growth stages of the abdominal aortic aneurysm, are predicted by means of Graph Neural Networks (GNNs). GNNs exploit the natural graph structure of the mesh obtained by the Finite Volume (FV) discretization, taking into account the spatial local information, regardless of the dimension of the input graph. Our experimental validation framework yields promising results, confirming our method as a valid alternative that overcomes the curse of dimensionality.

翻译：心血管系统的复杂性需要被精确再现，以便及时识别健康状况；为此，先进的**多保真度**与**多物理场**数值模型至关重要。一方面，**全阶模型**能提供精确的血流动力学评估，但其高昂的计算成本阻碍了其在临床实时应用。相比之下，**降阶模型**提供了高效且精确的解决方案，这对于个性化医疗和及时的临床决策至关重要。在本研究中，我们通过将**全阶模型**与**降阶模型**相结合，探索**计算流体动力学**在心血管医学中的应用，以预测主动脉瘤生长与破裂的风险。在腹主动脉瘤不同生长阶段采样的**壁面剪切应力**与**振荡剪切指数**，通过**图神经网络**进行预测。**图神经网络**利用了由**有限体积法**离散化所得网格的天然图结构，能够考虑空间局部信息，且不受输入图维度的限制。我们的实验验证框架取得了令人鼓舞的结果，证实了我们的方法是克服维度灾难的一种有效替代方案。