# Hybrid Physics-Based and Data-Driven Modeling of Vascular Bifurcation Pressure Differences

arXiv: https://arxiv.org/abs/2402.15651 · HTML: https://mlsanity.com/paper/2402.15651/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Feb 2024.

## Built on by (most cited first, 3 of 3)

- [Physics-constrained coupled neural differential equations for one dimensional blood flow modeling](https://mlsanity.com/paper/2411.05631/) (2024)
- [Data-Driven Bifurcation Handling in Physics-Based Reduced-Order Vascular Hemodynamic Models](https://mlsanity.com/paper/2508.21165/) (2025)
- [FalconBC: Flow matching for Amortized inference of Latent-CONditioned physiologic Boundary Conditions](https://mlsanity.com/paper/2603.19331/) (2026)

## Abstract (arXiv)

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.