# Data-driven Neural Networks for Windkessel Parameter Calibration

arXiv: https://arxiv.org/abs/2509.21206 · HTML: https://mlsanity.com/paper/2509.21206/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 72 references that are in the map.
- First posted on arXiv 25 Sep 2025.

## Builds on (most cited first, 5 of 5)

- [Physics-informed neural networks (PINNs) for fluid mechanics: A review](https://mlsanity.com/paper/2105.09506/) (2021)
- [An Intuitive Tutorial to Gaussian Process Regression](https://mlsanity.com/paper/2009.10862/) (2020)
- [Accelerated Patient-Specific Calibration via Differentiable Hemodynamics Simulations](https://mlsanity.com/paper/2412.14572/) (2024)
- [Bayesian calibration of Arterial Windkessel Model](https://mlsanity.com/paper/2201.06883/) (2022)
- [A Vector Fitting approach for the automated estimation of lumped boundary conditions of 1D circulation models](https://mlsanity.com/paper/2208.11482/) (2022)

## Abstract (arXiv)

In this work, we propose a novel method for calibrating Windkessel (WK) parameters in a dimensionally reduced 1D-0D coupled blood flow model. To this end, we design a data-driven neural network (NN)trained on simulated blood pressures in the left brachial artery. Once trained, the NN emulates the pressure pulse waves across the entire simulated domain, i.e., over time, space and varying WK parameters, with negligible error and computational effort. To calibrate the WK parameters on a measured pulse wave, the NN is extended by dummy neurons and retrained only on these. The main objective of this work is to assess the effectiveness of the method in various scenarios -- particularly, when the exact measurement location is unknown or the data are affected by noise.