A predictive surrogate model for hemodynamics and structural prediction in abdominal aorta for different physiological conditions.

This study investigates the application of a Predictive Surrogate Model (PSM) for the prediction of the fluid and solid variables in the abdominal aorta by integrating Proper Orthogonal Decomposition (POD) and Long Short-Term Memory (LSTM) techniques. The Fluid-Structure Interaction (FSI) solver, which serves as the Full-Order Model (FOM), can capture the blood hemodynamics and structural mechanics precisely for a variety of physiological states, namely the rest and exercise conditions. Detailed analyses have been conducted on velocity components, pressure, Wall Shear Stress (WSS), and Oscillatory Shear Index (OSI) variables. Firstly, the reconstruction error has been derived based on a specific number of POD bases to assess the Reduced Order Model (ROM). Notably, the reconstruction error for velocity components in the rest condition is one order of magnitude higher than that in the exercise condition, yet both remained below 10%. This error for pressure is even more minimal, being less than 1%. The PSM is evaluated against rest and exercise conditions, exhibiting promising results despite the inherent complexities of the physiological conditions. Despite the inherent complexities of phenomena in the aorta, the predictive model demonstrates consistent error magnitudes for velocity components and wall-related indices, while solid variables show slightly higher errors. • Predictive Surrogate Model (PSM) is used for predicting fluid and solid variables in the abdominal aorta. • POD and LSTM techniques are integrated into the PSM. • Detailed analyses are conducted on velocity components, pressure, WSS, and OSI variables. • The PSM is evaluated for rest and exercise conditions despite the complexities of physiological conditions. • The predictive model demonstrates consistent error magnitudes for velocity components and wall-related indices. [ABSTRACT FROM AUTHOR]