Physics-informed neural networks (PINNs) can be used to solve partial differential equations
(PDEs) and identify hidden variables by incorporating the governing equations into neural
network training. In this study, we apply PINNs to the assimilation of turbulent mean flow
data and investigate the method's ability to identify inaccessible variables and closure terms
from sparse data. Using high-fidelity large-eddy simulation data and particle image
velocimetry measured mean fields, we show that PINNs are suitable for simultaneously …