Author

Mattia Romero

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#machine learning Preprint Sep 2026

Drift Field Net: Learning Ocean Lagrangian advection fields from in-situ and satellite observations

DFN is a deep neural network that predicts ocean surface flow fields from operational satellite observations that reduces the mean positioning error after a 7-day forecast compared with the operational model and demonstrates the potential of deep learning for ocean surface flow prediction.

T. Archambault, Pierre Garcia, Mattia Romero et al. · 0 citations

Open access Aug 2026

CoDiT: conditional diffusion models for multi-day ocean drifter trajectory prediction

This work frames trajectory prediction as a denoising task and presents Conditional Diffusion models for Trajectories (CoDiT), which adapts the denoising diffusion framework, originally developed for image synthesis, to the problem of trajectory forecasting.

Christian Donner, Shirin Goshtasbpour, Emanuele Dalsasso et al. · Machine Learning: Earth · 0 citations