This project aims to revolutionize cardiovascular research and healthcare by developing an Al-augmented image-based hemodynamic modeling platform. The primary objective is to establish efficient and reliable data-enabled, patient-specific computational modeling capabilities to enhance the comprehensive understanding of cardiovascular physiology and pathophysiology. It addresses the gaps in existing image-based computational modeling frameworks that are labor-intensive, computationally expensive, and subject to large uncertainties. Specifically, the project will automate the transformation of medical images into precise 30 geometries for computational fluid dynamics (CFO) and fluid-structure interaction (FSI) simulations via deep learning, significantly reducing manual labor and computational costs. By integrating physics with mesh-based geometric deep learning through differentiable programming, this project aims to enable fast surrogate CFD/FSI simulations, epitomizing an innovative blend of machine learning with domain-specific knowledge and allowing for rapid predictions of functional information such as blood flow patterns, pressure and wall shear stresses. Furthermore, an ensemble Bayesian learning framework will be developed to propagate and quantify uncertainties in model predictions, thereby enhancing the reliability and trustworthiness of the results. Finally, the introduction of advanced visual analytics to interpret vast hemodynamic data ensembles underscores the project's commitment to accessibility and user-centric design. Collectively, these innovations aim to enhance the accuracy, efficiency, and reliability of patient-specific hemodynamic modeling, making them more accessible and actionable for healthcare professionals. The project's long-term goals include improving the understanding, diagnosis, and treatment of cardiovascular diseases, aligning with the National Heart, Lung, and Blood lnstitute's (NHLBI) mission to combat heart, lung, and blood diseases and extend the lives of those afflicted. By offering a novel approach to accurately and reliably model cardiovascular dynamics, the project holds the potential to significantly impact the field of cardiovascular research and healthcare, providing a basis for better clinical decisions and treatment strategies. RELEVANCE (See instructions): This research focuses on developing patient-specific models and diagnostic tools for cardiovascular diseases, aligning with the NHLBl's goal of improving health and extending life by furthering the knowledge and treatment of heart, lung, and blood conditions. By enhancing the accessibility and reliability of image-based computational modeling, the project will revolutionize cardiovascular healthcare, significantly advancing public health through improved prevention, diagnosis, and treatment.