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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 4
Nonlin. Processes Geophys., 26, 381–399, 2019
https://doi.org/10.5194/npg-26-381-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Nonlin. Processes Geophys., 26, 381–399, 2019
https://doi.org/10.5194/npg-26-381-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 05 Nov 2019

Research article | 05 Nov 2019

Generalization properties of feed-forward neural networks trained on Lorenz systems

Sebastian Scher and Gabriele Messori

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Status: closed
Status: closed
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 Sebastian Scher on behalf of the Authors (26 Sep 2019)  Author's response    Manuscript
ED: Publish as is (06 Oct 2019) by Stefano Pierini
Publications Copernicus
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Short summary
Neural networks are a technique that is widely used to predict the time evolution of physical systems. For this the past evolution of the system is shown to the neural network – it is trained – and then can be used to predict the evolution in the future. We show some limitations in this approach for certain systems that are important to consider when using neural networks for climate- and weather-related applications.
Neural networks are a technique that is widely used to predict the time evolution of physical...
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