Natalia Rubio
Ph.D. Student in Mechanical Engineering, admitted Autumn 2020
All Publications
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Learning reduced-order models for cardiovascular simulations with graph neural networks.
Computers in biology and medicine
2023; 168: 107676
Abstract
Reduced-order models based on physics are a popular choice in cardiovascular modeling due to their efficiency, but they may experience loss in 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 3% for pressure and flow rate, 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.
View details for DOI 10.1016/j.compbiomed.2023.107676
View details for PubMedID 38039892
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On the fractional Laplacian of variable order
FRACTIONAL CALCULUS AND APPLIED ANALYSIS
2022; 25 (1): 15-28
View details for DOI 10.1007/s13540-021-00003-1
View details for Web of Science ID 000810139800002