# Automated generation of 0D and 1D reduced-order models of patient-specific blood flow

arXiv: https://arxiv.org/abs/2111.04878 · HTML: https://mlsanity.com/paper/2111.04878/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Nov 2021.

## Built on by (most cited first, 15 of 15)

- [Learning Reduced-Order Models for Cardiovascular Simulations with Graph Neural Networks](https://mlsanity.com/paper/2303.07310/) (2023)
- [A Probabilistic Neural Twin for Treatment Planning in Peripheral Pulmonary Artery Stenosis](https://mlsanity.com/paper/2312.00854/) (2023)
- [Quantification of total uncertainty in the physics-informed reconstruction of CVSim-6 physiology](https://mlsanity.com/paper/2408.07201/) (2024)
- [Learning Hemodynamic Scalar Fields on Coronary Artery Meshes: A Benchmark of Geometric Deep Learning Models](https://mlsanity.com/paper/2501.09046/) (2025)
- [Deep learning-based surrogate model for 3-D patient-specific computational fluid dynamics](https://mlsanity.com/paper/2204.08939/) (2022)
- [InVAErt networks for amortized inference and identifiability analysis of lumped parameter hemodynamic models](https://mlsanity.com/paper/2408.08264/) (2024)
- [Data-driven discovery of mechanical models directly from MRI spectral data](https://mlsanity.com/paper/2411.06958/) (2024)
- [On the performance of multi-fidelity and reduced-dimensional neural emulators for inference of physiological boundary conditions](https://mlsanity.com/paper/2506.11683/) (2025)
- [GReAT: leveraging geometric artery data to improve wall shear stress assessment](https://mlsanity.com/paper/2508.19030/) (2025)
- [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)
- [Tokenised Flow Matching for Hierarchical Simulation Based Inference](https://mlsanity.com/paper/2604.20723/) (2026)
- [Same Branches, Different Trees: A Bifurcation Connectedness Metric for Coronary Artery Segmentation and FFR-CT Decision Agreement](https://mlsanity.com/paper/2607.28327/) (2026)
- [Real-Time Surrogate Modeling for Personalized Blood Flow Prediction and Hemodynamic Analysis](https://mlsanity.com/paper/2604.03197/) (2026)
- [Asymptotic-Preserving Neural Networks for Viscoelastic Parameter Identification in Multiscale Blood Flow Modeling](https://mlsanity.com/paper/2604.06287/) (2026)

## Abstract (arXiv)

Three-dimensional (3D) cardiovascular fluid dynamics simulations typically require hours to days of computing time on a high-performance computing cluster. One-dimensional (1D) and lumped-parameter zero-dimensional (0D) models show great promise for accurately predicting blood bulk flow and pressure waveforms with only a fraction of the cost. They can also accelerate uncertainty quantification, optimization, and design parameterization studies. Despite several prior studies generating 1D and 0D models and comparing them to 3D solutions, these were typically limited to either 1D or 0D and a singular category of vascular anatomies. This work proposes a fully automated and openly available framework to generate and simulate 1D and 0D models from 3D patient-specific geometries, automatically detecting vessel junctions and stenosis segments. Our only input is the 3D geometry; we do not use any prior knowledge from 3D simulations. All computational tools presented in this work are implemented in the open-source software platform SimVascular. We demonstrate the reduced-order approximation quality against rigid-wall 3D solutions in a comprehensive comparison with N=72 publicly available models from various anatomies, vessel types, and disease conditions. Relative average approximation errors of flows and pressures typically ranged from 1% to 10% for both 1D and 0D models, measured at the outlets of terminal vessel branches. In general, 0D model errors were only slightly higher than 1D model errors despite requiring only a third of the 1D runtime. Automatically generated ROMs can significantly speed up model development and shift the computational load from high-performance machines to personal computers.