The project focuses on fluid-structure interaction (FSI) in the cardiovascular system, with the aim of developing innovative numerical methods, supported by machine learning techniques, to obtain fast and accurate solutions. Three main challenges are addressed: the stability of weakly coupled numerical schemes, the reduction of computational complexity by means of reduced models (ROMs), and the efficient solution of clinically relevant inverse problems (shape optimization and parameter calibration).

The aim of the project is to improve the efficiency and accuracy of cardiovascular simulations, with clinical applications such as the prediction of the onset of arterial plaques and aneurysms and support in the planning of surgical interventions, such as bypasses.

The project aims to develop mathematical and computational tools useful for applications in real clinical settings: numerical strategies to improve the stability of weakly coupled FSI models, machine learning techniques for the automatic calibration of critical parameters in numerical models, reduced models that make real-time simulation feasible. Other expected results are the efficient resolution of inverse problems and the release of open source software (e.g., through the extension of libraries such as lifeX, ITHACA-FV, RBniCS). A dissemination effort of the results within the scientific community is also planned.