Sparse particle tracks can still yield turbulent flow fields: physics-constrained networks degrade more slowly as seeding thins
The German Aerospace Center team applied physics-informed neural networks (PINNs) to reconstruct Eulerian flow fields from sparse 3D Lagrangian particle tracking data, comparing them systematically with polynomial binning and FlowFit on identical preprocessed data. Across homogeneous isotropic turbulence, a synthetic turbulent boundary layer, and wind-tunnel boundary-layer measurements, PINNs gave the lowest velocity and pressure errors over the investigated sparsity levels and degraded more slowly as particle density fell. FlowFit achieved lower vorticity errors at several densely seeded conditions and kept its divergence residual near machine precision.