Search Results for author: Said Ouala

Found 9 papers, 2 papers with code

Online Calibration of Deep Learning Sub-Models for Hybrid Numerical Modeling Systems

no code implementations17 Nov 2023 Said Ouala, Bertrand Chapron, Fabrice Collard, Lucile Gaultier, Ronan Fablet

Most of these efforts in defining hybrid dynamical systems follow {offline} learning strategies in which the neural parameterization (called here sub-model) is trained to output an ideal correction.

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

no code implementations11 Feb 2022 Said Ouala, Steven L. Brunton, Ananda Pascual, Bertrand Chapron, Fabrice Collard, Lucile Gaultier, Ronan Fablet

The complexity of real-world geophysical systems is often compounded by the fact that the observed measurements depend on hidden variables.

Learning Runge-Kutta Integration Schemes for ODE Simulation and Identification

no code implementations11 May 2021 Said Ouala, Laurent Debreu, Ananda Pascual, Bertrand Chapron, Fabrice Collard, Lucile Gaultier, Ronan Fablet

Inevitably, a numerical simulation of a differential equation will then always be distinct from a true analytical solution.

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

1 code implementation4 Sep 2020 Duong Nguyen, Said Ouala, Lucas. Drumetz, Ronan Fablet

The data-driven recovery of the unknown governing equations of dynamical systems has recently received an increasing interest.

Learning Latent Dynamics for Partially-Observed Chaotic Systems

1 code implementation4 Jul 2019 Said Ouala, Duong Nguyen, Lucas. Drumetz, Bertrand Chapron, Ananda Pascual, Fabrice Collard, Lucile Gaultier, Ronan Fablet

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 observed, with an emphasis on forecasting applications, including long-term asymptotic patterns.

EM-like Learning Chaotic Dynamics from Noisy and Partial Observations

no code implementations25 Mar 2019 Duong Nguyen, Said Ouala, Lucas. Drumetz, Ronan Fablet

To solve for the joint inference of the hidden dynamics and of model parameters, we combine neural-network representations and state-of-the-art assimilation schemes.

Sea surface temperature prediction and reconstruction using patch-level neural network representations

no code implementations1 Jun 2018 Said Ouala, Cedric Herzet, Ronan Fablet

The forecasting and reconstruction of ocean and atmosphere dynamics from satellite observation time series are key challenges.

Numerical Integration Time Series +1

Bilinear residual Neural Network for the identification and forecasting of dynamical systems

no code implementations19 Dec 2017 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 dynamical systems for a wide range of applications, where model-driven models based on ordinary differential equation remain the state-of-the-art approaches.

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