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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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NPG | Articles | Volume 26, issue 3
Nonlin. Processes Geophys., 26, 143–162, 2019
https://doi.org/10.5194/npg-26-143-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Nonlin. Processes Geophys., 26, 143–162, 2019
https://doi.org/10.5194/npg-26-143-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 10 Jul 2019

Research article | 10 Jul 2019

Data assimilation as a learning tool to infer ordinary differential equation representations of dynamical models

Marc Bocquet et al.

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Status: closed
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AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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AR: Author's response | RR: Referee report | ED: Editor decision
AR by Marc Bocquet on behalf of the Authors (28 May 2019)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (31 May 2019) by Olivier Talagrand
RR by Anonymous Referee #1 (05 Jun 2019)
ED: Publish subject to technical corrections (11 Jun 2019) by Olivier Talagrand
AR by Marc Bocquet on behalf of the Authors (14 Jun 2019)  Author's response    Manuscript
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Short summary
This paper describes an innovative way to use data assimilation to infer the dynamics of a physical system from its observation only. The method can operate with noisy and partial observation of the physical system. It acts as a deep learning technique specialised to dynamical models without the need for machine learning tools. The method is successfully tested on chaotic dynamical systems: the Lorenz-63, Lorenz-96, and Kuramoto–Sivashinski models and a two-scale Lorenz model.
This paper describes an innovative way to use data assimilation to infer the dynamics of a...
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