Physics-informed deep learning for turbulent boundary layers: spatio-temporal reconstruction and prediction

This study introduces a physics-informed neural network (PINN) framework for the spatio-temporal reconstruction and prediction of turbulent boundary layer flow fields over a broad range of Reynolds numbers. By incorporating the governing N-S equations into the loss function, the proposed model effectively integrates physical constraints with sparse flow field data, enabling high-fidelity spatial reconstruction, resolution optimization, and temporal prediction of velocity and pressure fields. The method is validated using simulated shock-induced turbulent boundary layer data and experimental turbulent channel flow data. Results show that the PINN model accurately reconstructs flow fields with 60% missing data, infers pressure fields without pressure measurements, and provides reliable short-term temporal predictions. The framework exhibits strong generalizability and robustness, highlighting its potential for complex turbulent flow analysis in both computational and experimental fluid dynamics.