  Reduced-order models (ROMs) allow for the simulation of blood flow in
patient-specific vasculatures without the high computational cost and wait time
associated with traditional computational fluid dynamics (CFD) models.
Unfortunately, due to the simplifications made in their formulations, ROMs can
suffer from significantly reduced accuracy. One common simplifying assumption
is the continuity of static or total pressure over vascular junctions. In many
cases, this assumption has been shown to introduce significant error. We
propose a model to account for this pressure difference, with the ultimate goal
of increasing the accuracy of cardiovascular ROMs. Our model successfully uses
a structure common in existing ROMs in conjunction with machine-learning
techniques to predict the pressure difference over a vascular bifurcation. We
analyze the performance of our model on steady and transient flows, testing it
on three bifurcation cohorts representing three different bifurcation geometric
types. We also compare the efficacy of different machine-learning techniques
and two different model modalities.
