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Accelerated Patient-Specific Hemodynamic Simulations with Hybrid Physics-Based Neural Surrogates
Physics-based 0D reduced-order models provide computationally lightweight predictions of cardiovascular flows, resolving bulk hemodynamics in fractions of a second that would take days to solve using traditional 3D finite-element techniques. However, the accuracy of 0D models is limited as a result
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