Reduced order modeling (ROM) techniques are numerical methods that approximate the solution of parametric partial differential equation (PDE) by properly combining the high-fidelity solutions of the problem obtained for several configurations, i.e. for several properly chosen values of the physical/geometrical parameters characterizing the problem. In this contribution, we propose an efficient non-intrusive data-driven framework involving ROM techniques in computational fluid dynamics (CFD) for hemodynamics applications. By starting from a database of high-fidelity solutions related to a certain values of the parameters, we apply the proper orthogonal decomposition with interpolation (PODI) and then reconstruct the variables of interest for new values of the parameters, i.e. different values from the ones included in the database. Furthermore, we present a preliminary web application through which one can run the ROM with a very user-friendly approach, without the need of having expertise in the numerical analysis and scientific computing field. The case study we have chosen to test the efficiency of our algorithm is represented by the aortic blood flow pattern in presence of a Left Ventricular Assist Device (LVAD) when varying the pump flow rate.

翻译：减序建模(ROM)技术是数字方法,它通过适当结合为若干配置(即作为问题特点的物理/几何参数的若干适当选择的数值)而获得的问题的高度忠诚解决方案,接近于参数部分差异方程(PDE)的解决方案。我们为此建议了一个有效的非侵入性数据驱动框架,其中涉及计算流体动态(CFD)中的ROM技术,用于血液动力学应用。我们从一个与参数的某些值有关的高忠诚解决方案数据库开始,我们采用适当的或地表分解与内插(PODI),然后重建参数新值的兴趣变量,即不同于数据库中所包含的值。此外,我们提出了一个初步的网络应用程序,通过这个应用程序,一个人可以使用非常方便用户的方法运行ROM,而不需要在数字分析和科学计算领域的专门知识。我们选择测试我们算法效率的案例研究,是在左方轴辅助设备存在时,以流动率变化的反动血流模式为代表。