  We address Lagrangian drift simulation in geophysical dynamics and explore
deep learning approaches to overcome known limitations of state-of-the-art
model-based and Markovian approaches in terms of computational complexity and
error propagation. We introduce a novel architecture, referred to as DriftNet,
inspired from the Eulerian Fokker-Planck representation of Lagrangian dynamics.
Numerical experiments for Lagrangian drift simulation at the sea surface
demonstrates the relevance of DriftNet w.r.t. state-of-the-art schemes.
Benefiting from the fully-convolutional nature of Drift-Net, we explore through
a neural inversion how to diagnose modelderived velocities w.r.t. real drifter
trajectories.
