Efficient and accurate reduced order modelling for cardiovascular applications
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Heart disease is one of the main cause of death worldwide. Therefore in the last years the biomedical community has shown a growing attention towards the simulation of the blood flow dynamics by means of numerical methods. The Full Order Models (FOMs) can be adopted in principle to investigate the blood flow dynamics in patient-specific cases. However this would require a lot of simulations by leading to a prohibitive computational cost. Our work is focused on the development of Reduced Order Models (ROMs) which are specifically formulated to enhance the computational efficiency without a significant loss in terms of accuracy. Both projection-based and data-driven techniques are adopted for patient-specific applications and a comparison between FOM and ROM in terms of efficiency and accuracy is addressed.
