39 citations · 133 across the 11 of their papers we have counts for
11 papers
Towards fully differentiable neural ocean model with Veros
Etienne Meunier, Said Ouala, Hugo Frezat +2
We present a differentiable extension of the VEROS ocean model, enabling automatic differentiation through its dynamical core. We describe the key modifications required to make th…
AI for Extreme Event Modeling and Understanding: Methodologies and Challenges
Gustau Camps-Valls, Miguel-Ángel Fernández-Torres, Kai-Hendrik Cohrs +22
In recent years, artificial intelligence (AI) has deeply impacted various fields, including Earth system sciences. Here, AI improved weather forecasting, model emulation, parameter…
Online Calibration of Deep Learning Sub-Models for Hybrid Numerical Modeling Systems
Said Ouala, Bertrand Chapron, Fabrice Collard +2
Artificial intelligence and deep learning are currently reshaping numerical simulation frameworks by introducing new modeling capabilities. These frameworks are extensively investi…
Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review
Sibo Cheng, Cesar Quilodran-Casas, Said Ouala +14
Data Assimilation (DA) and Uncertainty quantification (UQ) are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics. Typical a…
Bounded nonlinear forecasts of partially observed geophysical systems with physics-constrained deep learning
Said Ouala, Steven L. Brunton, Ananda Pascual +4
The complexity of real-world geophysical systems is often compounded by the fact that the observed measurements depend on hidden variables. These latent variables include unresolve…
Learning Runge-Kutta Integration Schemes for ODE Simulation and Identification
Said Ouala, Laurent Debreu, Ananda Pascual +4
Deriving analytical solutions of ordinary differential equations is usually restricted to a small subset of problems and numerical techniques are considered. Inevitably, a numerica…
Variational Deep Learning for the Identification and Reconstruction of Chaotic and Stochastic Dynamical Systems from Noisy and Partial Observations
Duong Nguyen, Said Ouala, Lucas Drumetz +1
The data-driven recovery of the unknown governing equations of dynamical systems has recently received an increasing interest. However, the identification of governing equations re…
Learning Latent Dynamics for Partially-Observed Chaotic Systems
Said Ouala, Duong Nguyen, Lucas Drumetz +5
This paper addresses the data-driven identification of latent dynamical representations of partially-observed systems, i.e., dynamical systems for which some components are never o…
EM-like Learning Chaotic Dynamics from Noisy and Partial Observations
Duong Nguyen, Said Ouala, Lucas Drumetz +1
The identification of the governing equations of chaotic dynamical systems from data has recently emerged as a hot topic. While the seminal work by Brunton et al. reported proof-of…
Sea surface temperature prediction and reconstruction using patch-level neural network representations
Said Ouala, Cedric Herzet, Ronan Fablet
The forecasting and reconstruction of ocean and atmosphere dynamics from satellite observation time series are key challenges. While model-driven representations remain the classic…
Bilinear residual Neural Network for the identification and forecasting of dynamical systems
Ronan Fablet, Said Ouala, Cedric Herzet
Due to the increasing availability of large-scale observation and simulation datasets, data-driven representations arise as efficient and relevant computation representations of dy…