Articles | Volume 26, issue 4
Nonlin. Processes Geophys., 26, 381–399, 2019
Nonlin. Processes Geophys., 26, 381–399, 2019

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


Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

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
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.