Institute of Mechanics

Particle transport in complex flows is usually obtained by solving the Navier–Stokes equations, and numerical or experimental routes demand deep physical insight and high computational cost, while existing machine-learning alternatives fail to generalize long-range particle trajectories under sparse data. TrajectoryFlowNet couples a Lagrangian view of particle tracks with a Eulerian view of full velocity and pressure fields inside one physics-constrained network, so a few trajectory measurements yield both the full-field flow and long-range paths for arbitrary particles. It is validated on numerical lid-driven cavity and complex cylinder flows and on experimental aortic and ventricle blood flows, keeping predictions physically consistent in flows with irregular boundaries.

Particle transport in complex flows is usually obtained by solving the Navier–Stokes equations, and numerical or experimental routes demand deep physical insight and high computational cost, while existing machine-learning alternatives fail to generalize long-range particle trajectories under sparse data. TrajectoryFlowNet couples a Lagrangian view of particle tracks with a Eulerian view of full velocity and pressure fields inside one physics-constrained network, so a few trajectory measurements yield both the full-field flow and long-range paths for arbitrary particles. It is validated on numerical lid-driven cavity and complex cylinder flows and on experimental aortic and ventricle blood flows, keeping predictions physically consistent in flows with irregular boundaries.