Data-driven Neural Networks for Windkessel Parameter Calibration
This work addresses parameter calibration in cardiovascular modeling, which is incremental as it applies existing neural network techniques to a specific domain problem.
The authors tackled the problem of calibrating Windkessel parameters in a blood flow model by proposing a data-driven neural network trained on simulated pressures, achieving negligible error and low computational effort in emulating pressure waves. They extended the method to handle scenarios with unknown measurement locations or noisy data.
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.