CAREER: Synergistic physics-based and deep learning cardiovascular flow modeling

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Accurate quantification of blood flow across different scales is crucial to our fundamental understanding of cardiovascular disease and clinical decision making. While computational and experimental blood flow modeling has seen tremendous progress, we still have difficulty generating reliable data. Low resolutions and unknown parameters overburden high fidelity modeling. Additionally, blood flow dynamics near the wall where disease localizes are hard to quantify due to thin boundary layers and challenges in near-wall transport modeling. Finally, the large datasets that blood flow models produce are difficult to efficiently store and interpret. This project will develop software that synergistically integrates novel deep learning and traditional physics-based modeling to address these issues. The software will promote the progress of scientific cardiovascular disease and fluid flow modeling research and ultimately advance national health. The project will create new education programs integrated with research to promote fluid flow computer modeling education by blending data analysis, visualization, and computer modeling. The education program will be integrated with regional initiatives to promote STEM participation in underrepresented groups.This CAREER program is built on four overarching goals. First, physics informed neural network (PINN) models will be used to overcome inherent blood flow modeling limitations. Second, the models will be used for gaining a physical understanding of blood flow patterns across different scales. Auxiliary PINN models will be defined to bridge blood flow patterns away and near the vessel wall and understand the minimal data collection necessary for near-wall blood flow modeling. Subsequently, a hybrid computational fluid dynamics (CFD) and PINN model will be developed for on-the-fly CFD and deep learning modeling. The goal is to compress and store the wealth of information that is generated and often ignored by CFD solvers and tackle difficult multiscale problems that challenge traditional CFD approaches. Finally, an education program called FAST (Fluids, Art, and StoryTelling!) will be developed to generate enthusiasm for integrated computer modeling and engineering education. The goal is to leverage the art of visualization and storytelling to show the hidden beauty in fluid mechanics computer modeling. This CAREER program will build a foundation for hybrid and complementary deep learning and physics-based modeling approaches that advance fundamental and transformative blood flow modeling research and will enable lifetime leadership in integrating research with education.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.