Augmentation of experimentally obtained flow fields by means of Physics Informed Neural Networks (PINN) demonstrated on aneurysm flow

Abstract

Biofluid mechanics play an important role in the study of the mechanism of cardiovascular diseases and in the development of new implants. For the assessment of hydrodynamic parameters, experimental methods as well as in-silico approaches can be used, such as particle image velocimetry (PIV) and Deep Learning, respectively. Challenges for PIV are the optical access to the region of interest, and time consumption for measuring and post-processing analysis in particular for three dimensional flow. To overcome these limitations state-of-the-art deep learning algorithms could be utilized to augment spatially coarse resolved flow fields. In this study, we demonstrate the use of Physics Informed Neural Networks (PINN) to augment PIV measurement data. To demonstrate a combined workflow, we investigate the flow of a Newtonian fluid through a simplified aneurysm under laminar conditions. Generation of synthetic PIV particle images of a single measurement plane and the corresponding PIV vector calculations were performed as the basis for the PINN algorithm. Based on the Navier-Stokes equations the PINN reconstructs the entire 3D flow field and pressure distribution inside the aneurysm. We observed qualitative agreements between ground through data and PINN predictions. Nevertheless, there are substantial differences in the quantitative, locally resolved comparison of the flow metrics, despite the generally tendency for the PINN algorithm to correctly augment the flow field.

Keywords

• physics informed neural networks
• deep learning
• flow field augmentation
• aneurysm
• particle image velocimetry