ArXiv Preprint
Reduced-order models based on physics are a popular choice in cardiovascular
modeling due to their efficiency, but they may experience reduced accuracy when
working with anatomies that contain numerous junctions or pathological
conditions. We develop one-dimensional reduced-order models that simulate blood
flow dynamics using a graph neural network trained on three-dimensional
hemodynamic simulation data. Given the initial condition of the system, the
network iteratively predicts the pressure and flow rate at the vessel
centerline nodes. Our numerical results demonstrate the accuracy and
generalizability of our method in physiological geometries comprising a variety
of anatomies and boundary conditions. Our findings demonstrate that our
approach can achieve errors below 2% and 3% for pressure and flow rate,
respectively, provided there is adequate training data. As a result, our method
exhibits superior performance compared to physics-based one-dimensional models,
while maintaining high efficiency at inference time.
Luca Pegolotti, Martin R. Pfaller, Natalia L. Rubio, Ke Ding, Rita Brugarolas Brufau, Eric Darve, Alison L. Marsden
2023-03-13
