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Abstract Predicting particle transport in complex flows is traditionally achieved by solving the Navier–Stokes equations. Numerical and experimental methods typically require deep physical insights and incur high computational costs. Although machine learning offers an alternative, existing approaches fail to generalize the prediction of long-range particle trajectories in sparse data regimes.
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