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

Bocquet, Marc; Brajard, Julien; Carrassi, Alberto; Bertino, Laurent

doi:https://doi.org/10.5194/npg-26-143-2019

Articles | Volume 26, issue 3

https://doi.org/10.5194/npg-26-143-2019

© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/npg-26-143-2019

© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 26, issue 3

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, Julien Brajard, Alberto Carrassi, and Laurent Bertino

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Final revised paper (published on 10 Jul 2019)
Preprint (discussion started on 28 Feb 2019)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Comments on "Data assimilation as a deep learning tool to infer ODE representations of dynamical models" by Bocquet et al.', Anonymous Referee #1, 03 Apr 2019
- AC1: 'Response to Referee #1', Marc Bocquet, 28 May 2019
RC2: 'Review of "Data assimilation as a deep learning tool to infer ODE representations of dynamical models” by Marc Bocquet et al.', Anonymous Referee #2, 04 May 2019
- AC2: 'Response to Referee #2', Marc Bocquet, 28 May 2019
EC1: 'Comments by Editor', Olivier Talagrand, 07 May 2019
- AC3: 'Response to the Editor', Marc Bocquet, 28 May 2019

Peer-review completion

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

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.