Hybrid modeling of the human cardiovascular system using NeuralFMUs

TL;DR

This paper introduces NeuralFMUs, a hybrid modeling approach combining first principles and machine learning to improve cardiovascular system simulations, reducing modeling complexity and enhancing computational efficiency.

Contribution

NeuralFMUs enable integration of diverse first principle models with neural networks, simplifying hybrid modeling of complex systems like the human cardiovascular system.

Findings

Hybrid models are easier to develop and less error-prone.

The hybrid approach improves computational performance.

Accuracy of hemodynamic quantities is maintained.

Abstract

Hybrid modeling, the combination of first principle and machine learning models, is an emerging research field that gathers more and more attention. Even if hybrid models produce formidable results for academic examples, there are still different technical challenges that hinder the use of hybrid modeling in real-world applications. By presenting NeuralFMUs, the fusion of a FMU, a numerical ODE solver and an ANN, we are paving the way for the use of a variety of first principle models from different modeling tools as parts of hybrid models. This contribution handles the hybrid modeling of a complex, real-world example: Starting with a simplified 1D-fluid model of the human cardiovascular system (arterial side), the aim is to learn neglected physical effects like arterial elasticity from data. We will show that the hybrid modeling process is more comfortable, needs less system knowledge and is therefore less error-prone compared to modeling solely based on first principle. Further, the resulting hybrid model has improved in computation performance, compared to a pure first principle white-box model, while still fulfilling the requirements regarding accuracy of the considered hemodynamic quantities. The use of the presented techniques is explained in a general manner and the considered use-case can serve as example for other modeling and simulation applications in and beyond the medical domain.