A predictive surrogate model based on linear and nonlinear solution manifold reduction in cardiovascular FSI: A comparative study

Abstract

This study investigates the fluid-structure interaction (FSI) simulation of the abdominal aorta, with a particular focus on the hemodynamic alterations induced by aneurysmal deformations. The hemodynamic behavior within the aorta is highly dependent on the geometric characteristics of the aneurysm, necessitating the use of patient-specific models to ensure accurate predictions. The primary objective of this research is to enhance the predictive capability of flow and structural indices in a complex FSI biomechanical setting under varying physiological conditions, namely rest and exercise states. This paper presents a comparative analysis between two distinct yet promising surrogate models: Proper Orthogonal Decomposition coupled with Long Short-Term Memory (POD + LSTM) and Convolutional Neural Network combined with Long Short-Term Memory (CNN + LSTM). The methodology, model selection, and comparative performance analysis are discussed in detail, providing insights into the efficacy and limitations of each approach in the context of personalized cardiovascular simulations. • Proposes a novel surrogate modeling framework for FSI in the abdominal aorta under rest and exercise states. • Compares two distinct approaches (POD + LSTM and CNN + LSTM) for predictive computational efficiency. • Demonstrates how aneurysmal deformations significantly influence hemodynamic indices. • Shows that both surrogate models can reduce simulation time while maintaining robust predictive performance.

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