Mathematics

Diseases of the cardiovascular system account for a large percentage of deaths worldwide every year. In many cases computational fluid dynamics studies can aid in a better understanding of the underlying phenomena. However, a successful day-to-day exploitation of computational tools in a clinical environment presents two main challenges: (i) reliable results require to integrate patient-specific data, and (ii) computational times should be as small as possible, without affecting the accuracy of the obtained results. In this talk we will describe a computational reduced order methodology (based on POD-Galerkin reduction methods) to tackle these aspects, and show a couple of representative studies.