Tracking hemodynamic responses to treatment and stimuli over long periods remains a grand challenge. Moving from established single-heartbeat technology to longitudinal profiles would require continuous data describing how the patient's state evolves new methods to extend the temporal domain over which flow is sampled and high-throughput computing resources. While personalized digital twins can accurately measure 3D hemodynamics over several heartbeats state-of-the-art methods would require 250 years of wallclock time on leadership scale systems to simulate one month of activity. To address these challenges we propose a cloud-based parallel-in-time framework leveraging continuous data from wearable devices to capture the first 3D patient-specific longitudinal hemodynamic maps. We demonstrate the validity of our method by establishing ground truth data for 750 beats (the longest simulation of 3D coronary flow to date) and comparing results. Our cloud-based framework relies on an initial fixed set of simulations to enable the wearable-informed creation of personalized longitudinal hemodynamic maps.
