activity
20172021
most citedEM-like Learning Chaotic Dynamics from Noisy and Partial Observations

24 citations · 43 across the 2 of their papers we have counts for

collaborators

6 papers

math.NA2021

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…

cs.LG2020

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…

stat.ML2019

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…

cs.LG201924 cited

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…

stat.ML2018

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…

cs.LG201719 cited

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…