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.
@article{arxiv.2509.21206,
title = {Data-driven Neural Networks for Windkessel Parameter Calibration},
author = {Benedikt Hoock and Tobias Köppl},
journal= {arXiv preprint arXiv:2509.21206},
year = {2025}
}
Comments
32 pages, 15 figures, for associated git see https://github.com/bhoock/WKcalNN, submitted to International Journal for Numerical Methods in Biomedical Engineering