Deep learning for Lagrangian drift simulation at the sea surface
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 9 of its 37 references that are in the map. First posted on arXiv 17 Nov 2022.
Builds on
9 of 37 references.
References · 9
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- 2017Parcels v0.9: prototyping a Lagrangian Ocean Analysis framework for the petascale agecited by 2
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Citing papers per year: 2026: 1.
1 paper.