Recent progress in machine learning (ML), together with advanced computational power,
have provided new research opportunities in cardiovascular modeling. While classifying …

Mathematical and computational modeling of the cardiovascular system is increasingly
providing non‐invasive alternatives to traditional invasive clinical procedures. Moreover, it …

Image-based computational fluid dynamics (CFD) modeling enables derivation of
hemodynamic information (eg, flow field, wall shear stress, and pressure distribution), which …

Background: Physics-informed neural networks (PINNs) have emerged as a powerful tool for
solving inverse problems, especially in cases where no complete information about the …

Parameter estimation in blood flow models from measured velocity data—as eg velocity-
encoded MRI—is a key step for patient-specific hemodynamic analysis. However, velocity …

We present a penalization parameter method for obstacle identification in an incompressible
fluid flow for a modified version of the Oseen equations. The proposed method consists in …

One of the crucial aspects of patient-specific blood flow simulations is to specify material
parameters and boundary conditions. The choice of boundary conditions can have a …

The significant progress in computer innovation technique, computational fluid dynamics
(CFD) has been applied for real applications on patient-specific hemodynamics and …

We develop error estimates for the finite element approximation of elliptic partial differential
equations on perturbed domains, ie when the computational domain does not match the real …

The research presented in this thesis focuses on the mathematical modelling, analysis, and
development of numerical schemes to capture complex flows. We present results on …