Reduced Order Models
3D computational fluid dynamics (CFD) simulations typically require hours or even days of computing time on a high-performance cluster. In contrast, reduced-order models (ROMs) can deliver fast yet accurate approximations of quantities of interest. Such models hold independent value and can accelerate uncertainty quantification and optimization studies. Physics-based ROMs simplify assumptions about the model physics, e.g., 0D and 1D fluid dynamics models (see picture). Data-driven models leverage data from past CFD solutions to train computationally lightweight models (e.g., neural networks) that predict the flow fields produced by new anatomies and flow conditions.
Lab members involved in the project: Luca Pegolotti, Martin Pfaller, Jonathan Pham, Natalia Rubio