# Deep learning for Lagrangian drift simulation at the sea surface

arXiv: https://arxiv.org/abs/2211.09818 · HTML: https://mlsanity.com/paper/2211.09818/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 37 references that are in the map.
- First posted on arXiv 17 Nov 2022.

## Builds on (most cited first, 9 of 9)

- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [PDE-Net: Learning PDEs from Data](https://mlsanity.com/paper/1710.09668/) (2017)
- [TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents](https://mlsanity.com/paper/1811.02146/) (2018)
- [GeoTrackNet-A Maritime Anomaly Detector using Probabilistic Neural Network Representation of AIS Tracks and A Contrario Detection](https://mlsanity.com/paper/1912.00682/) (2019)
- [TridentNet: A Conditional Generative Model for Dynamic Trajectory Generation](https://mlsanity.com/paper/2101.06374/) (2021)
- [A DNN Framework for Learning Lagrangian Drift With Uncertainty](https://mlsanity.com/paper/2204.05891/) (2022)
- [Parcels v0.9: prototyping a Lagrangian Ocean Analysis framework for the petascale age](https://mlsanity.com/paper/1707.05163/) (2017)
- [Conditional Generative Adversarial Networks for Speed Control in Trajectory Simulation](https://mlsanity.com/paper/2103.11471/) (2021)

## Built on by (most cited first, 1 of 1)

- [Impact of geophysical fields on Deep Learning-based Lagrangian drift simulations](https://mlsanity.com/paper/2604.03292/) (2026)

## 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.