# Data-Driven Bifurcation Handling in Physics-Based Reduced-Order Vascular Hemodynamic Models

arXiv: https://arxiv.org/abs/2508.21165 · HTML: https://mlsanity.com/paper/2508.21165/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 118 references that are in the map.
- First posted on arXiv 28 Aug 2025.

## Builds on (most cited first, 11 of 11)

- [Learning Reduced-Order Models for Cardiovascular Simulations with Graph Neural Networks](https://mlsanity.com/paper/2303.07310/) (2023)
- [Automated generation of 0D and 1D reduced-order models of patient-specific blood flow](https://mlsanity.com/paper/2111.04878/) (2021)
- [Real-time simulation of parameter-dependent fluid flows through deep learning-based reduced order models](https://mlsanity.com/paper/2106.05722/) (2021)
- [Data-driven reduced-order modelling for blood flow simulations with geometry-informed snapshots](https://mlsanity.com/paper/2302.11006/) (2023)
- [Improved multifidelity Monte Carlo estimators based on normalizing flows and dimensionality reduction techniques](https://mlsanity.com/paper/2312.12361/) (2023)
- [On the periodicity of cardiovascular fluid dynamics simulations](https://mlsanity.com/paper/2102.00107/) (2021)
- [Personalized and uncertainty-aware coronary hemodynamics simulations: From Bayesian estimation to improved multi-fidelity uncertainty quantification](https://mlsanity.com/paper/2409.02247/) (2024)
- [svMorph: Interactive geometry-editing tools for virtual patient-specific vascular anatomies](https://mlsanity.com/paper/2210.07087/) (2022)
- [Hybrid Physics-Based and Data-Driven Modeling of Vascular Bifurcation Pressure Differences](https://mlsanity.com/paper/2402.15651/) (2024)
- [Comparison of 1D and 3D Models for the Estimation of Fractional Flow Reserve](https://mlsanity.com/paper/1805.11472/) (2018)
- [A data-driven Reduced Order Method for parametric optimal blood flow control: application to coronary bypass graft](https://mlsanity.com/paper/2206.15384/) (2022)

## Built on by (most cited first, 1 of 1)

- [A Multi-Agent Framework for Zero-Dimensional Reduced-Order Model Planning](https://mlsanity.com/paper/2607.10994/) (2026)

## Abstract (arXiv)

Three-dimensional (3D) finite-element simulations of cardiovascular flows provide high-fidelity predictions to support cardiovascular medicine, but their high computational cost limits clinical practicality. Reduced-order models (ROMs) offer computationally efficient alternatives but suffer reduced accuracy, particularly at vessel bifurcations where complex flow physics are inadequately captured by standard Poiseuille flow assumptions. We present an enhanced numerical framework that integrates machine learning-predicted bifurcation coefficients into zero-dimensional (0D) hemodynamic ROMs to improve accuracy while maintaining computational efficiency. We develop a resistor-resistor-inductor (RRI) model that uses neural networks to predict pressure-flow relationships from bifurcation geometry, incorporating linear and quadratic resistances along with inductive effects. The method employs non-dimensionalization to reduce training data requirements and apriori flow split prediction for improved bifurcation characterization. We incorporate the RRI model into a 0D model using an optimization-based solution strategy. We validate the approach in isolated bifurcations and vascular trees, across Reynolds numbers from 0 to 5,500, defining ROM accuracy by comparison to 3D finite element simulation. Results demonstrate substantial accuracy improvements: averaged across all trees and Reynolds numbers, the RRI method reduces inlet pressure errors from 54 mmHg (45%) for standard 0D models to 25 mmHg (17%), while a simplified resistor-inductor (RI) variant achieves 31 mmHg (26%) error. The enhanced 0D models show particular effectiveness at high Reynolds numbers and in extensive vascular networks. This hybrid numerical approach enables accurate, real-time hemodynamic modeling for clinical decision support, uncertainty quantification, and digital twins in cardiovascular biomedical engineering.