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Neural partial differential equations for chaotic systems

Urheber*innen
/persons/resource/gelbrecht

Gelbrecht,  Maximilian
Potsdam Institute for Climate Impact Research;

/persons/resource/Niklas.Boers

Boers,  Niklas
Potsdam Institute for Climate Impact Research;

/persons/resource/Juergen.Kurths

Kurths,  Jürgen
Potsdam Institute for Climate Impact Research;

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25408oa.pdf
(Verlagsversion), 6MB

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Zitation

Gelbrecht, M., Boers, N., Kurths, J. (2021): Neural partial differential equations for chaotic systems. - New Journal of Physics, 23, 043005.
https://doi.org/10.1088/1367-2630/abeb90


Zitierlink: https://publications.pik-potsdam.de/pubman/item/item_25408
Zusammenfassung
When predicting complex systems one typically relies on differential equation which can often be incomplete, missing unknown infl uences or higher order effects. By augmenting the equations with artificial neural networks we can compensate these deficiencies. We show that this can be used to predict paradigmatic, high-dimensional chaotic partial differential equations even when only short and incomplete datasets are available. The forecast horizon for these high dimensional systems is about an order of magnitude larger than the length of the training data.