(For a more detailed list of publications of AI Chair OceaniX go to Google Scholar, ResearchGate)

Group highlights

Bounded nonlinear forecasts of partially observed geophysical systems with physics-constrained deep learning

The identification of computationally-relevant representations of partially-observed and highly nonlinear systems is challenging and often limited to short-term forecast applications. Here, we investigate the physics-constrained learning of implicit dynamical embeddings, leveraging neural ordinary differential equation representations under boundedness constraint.

S. Ouala, S. Brunton, A. Pascual, B. Chapron, F. Collard, L. Gaultier, R. Fablet

arXiv, 2022.

Learning Variational Data Assimilation Models and Solvers

This paper introduces 4DVarNet, an end-to-end learning approach for variational data assimilation models and solvers. Reported results may question the design of operational data assimilation systems.

R Fablet, B. Chapron, L. Drumetz, E. Memin, O. Pannekoucke, F. Rousseau

JAMES, 2021

Generative Adversarial Networks (GAN) for the simulation of central-place foraging trajectories

In this work, we investigate Generative Adversarial Networks (GAN) - for the simulation of animal trajectories. We demonstrate the outstanding ability of GANs to simulate ‘realistic’ seabirds foraging trajectories.

A. Roy, S. Lanco Bertrand, R. Fablet

Methods in Ecol. Evol., 2022

A posteriori learning of quasi-geostrophic turbulence parametrization: an experiment on integration steps

Modeling the subgrid-scale dynamics of reduced models is a long standing open problem that finds application in ocean, atmosphere and climate predictions where direct numerical simulation (DNS) is impossible. While neural networks (NNs) have already been applied to a range of three-dimensional flows with success, two dimensional flows are more challenging because of the backscatter of energy from small to large scales. We show that learning a model jointly with the dynamical solver and a meaningful a-posteriori-based loss function lead to stable and realistic simulations when applied to quasi-geostrophic turbulence.

Frezat, H., Sommer, J. L., Fablet, R., Balarac, G., Lguensat, R.

ML4PS, 2021.

Learning Runge-Kutta Integration Schemes for ODE Simulation and Identification

In this paper, we propose a novel framework to learn integration schemes that minimize an integration-related cost function. Especially, we demonstrate the relevance of the proposed learning-based approach for non-linear equations.

S. Ouala, L. Debreu, A. Pascual, B. Chapron, F. Collard, L. Gaultier, R. Fablet


TrAISformer. A generative transformer for AIS trajectory prediction

This paper presents TrAISformer-a generative transformer for AIS trajectory prediction. Compared with state-of-the-art approachs, it shows a great capacity to forecast maritime traffic trajectories few hours ahead in complex maritime traffic areas.

D. Nguyen, R Fablet


Joint Interpolation and Representation Learning for Irregularly Sampled Satellite-Derived Geophysical Fields

This paper introduces an end-to-end architecture for the joint interpolation and representation learning for irregularly-sampled observation data. As application case-studies, we consider ocean remote sensing datasets, which involve very large missing data rates.

R Fablet, L. Drumetz, M. Beauchamp, F. Rousseau

Front. in Applied Math and Statistics

Unsupervised Reconstruction of Sea Surface Currents from AIS Maritime Traffic Data Using Trainable Variational Models

In this work we investigate the relevance of AIS data streams for the estimation of the surface current velocities. Using a physics-informed observation model, we propose to a trainable variational formulation. Numerical experiments on a real AIS dataset off South Africa support the relevance of the proposed approach to improve the reconstruction of sea surface current, including w.r.t. altimetry-based ones.

S. Benaïchouche, C. Legoff, Y. Guichoux, F. Rousseau, R. Fablet

Remote Sensing, 2021

Deep Learning and Trajectory Representation for the Prediction of Seabird Diving Behaviour

We propose a novel deep learning model for seabird trajectory data. Our CNN model considerably increases the ability of deep networks to infer seabird behaviours, as well as their stability to different data inputs.

A. Roy, S. Lanco-Bertrand, R Fablet

PLoS Comp. Biol., 2022.

End-to-end physics-informed representation learning for satellite ocean remote sensing data: applications to satellite altimetry and sea surface currents

This paper presents different applications of 4DVarNN framework to satellite-derived sea surface dynamics, including learning-based optimal sampling strategies.

R Fablet, M.M. Amar, Q. Febvre, M. Beauchamp, B. Chapron

Proc. ISPRS Congress 2021

Learning Latent Dynamics for Partially-Observed Chaotic Systems

This paper introduces a novel data driven framework for the identification of ODE representations for partially observed systems.

S Ouala, D Nguyen, L Drumetz, B Chapron, A Pascual, F Collard, L Gaultier, R Fablet

Chaos, 2020.

Physical invariance in neural networks for subgrid-scale scalar flux modeling

Transformation invariances are shown to improve performance and generalization of NN models in the context of subgrid-scale turbulent modeling.

H Frezat, G Balarac, J Le Sommer, R Fablet, R Lguensat

Phys. Rev. Fluids, 2020.

Variational Deep Learning for the Identification and Reconstruction of Chaotic and Stochastic DynamicalSystems from Noisy and Partial Observations

Bridging state-of-the-art data assimilation and machine learning techniques to jointly reconstruct the true states and identify the governing equations of dynamical systems from series of noisy and partial data.

D Nguyen, S Ouala, L Drumetz, R Fablet


Joint learning of variational models and solvers for inverse problems

This paper introduces a novel data-driven framework to jointly learn variational models and the associated solvers to address inverse problems in signal and image processing, especially considering irregulary-sampled observation data.

R Fablet, L Drumetz, F Rousseau


PDE-NetGen 1.0: from symbolic partial differential equation (PDE) representations of physical processes to trainable neural network representations

A first paper from the collaboration with O. Pannekoucke (Meteo France) addressing the automatic generation of neural network architectures from symbolic PDEs.

O Pannekoucke, R Fablet

GMD (2020)

GeoTrackNet-A Maritime Anomaly Detector using Probabilistic Neural Network Representation of AIS Tracks and A Contrario Detection

The presentation of GeoTrackNet framework for the detection of abnormal behaviours in AIS-based maritime traffic surveillance using variational deep learning.

D Nguyen, R Vadaine, G Hajduch, R Garello, R Fablet

IEEE TITS, 2020 (arXiv version)