Assimilation-Based Learning of Chaotic Dynamical Systems from Noisy and Partial Data

Duong Nguyen; Said Ouala; Lucas Drumetz; Ronan Fablet

ICASSP 2020accepted0 citations

Assimilation-Based Learning of Chaotic Dynamical Systems from Noisy and Partial Data

Duong Nguyen, Said Ouala, Lucas Drumetz, Ronan Fablet

Abstract

Despite some promising results under ideal conditions (i.e. noise-free and complete observation), learning chaotic dynamical systems from real life data is still a very challenging task. We propose a novel framework, which combines data assimilation schemes and neural network representation, namely Auto-Encoders and Ensemble Kalman Smoother, to learn the governing equations of dynamical systems. By treating the learning as a Bayesian estimation problem, our framework can deal with noisy and partial observations. Experiments on the chaotic Lorenz-63 dynamics with different noise settings demonstrate the advantages of our method over the state-of-the-art.

BibTeX

@inproceedings{icassp2020_assimilationbase,
  title = {Assimilation-Based Learning of Chaotic Dynamical Systems from Noisy and Partial Data},
  author = {Duong Nguyen and Said Ouala and Lucas Drumetz and Ronan Fablet},
  booktitle = {ICASSP 2020},
  year = {2020}
}