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