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Drift Field Net (DFN) is a deep neural network that predicts ocean surface flow fields from satellite observations, using a two-stage training strategy to improve particle trajectory accuracy. It reduces positioning errors by 20 km in 7-day forecasts compared to operational models, with further gains from Lagrangian fine-tuning.