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Journal Article

Neural Partial Differential Equations for Chaotic Systems

Authors
/persons/resource/gelbrecht

Gelbrecht,  Maximilian
Potsdam Institute for Climate Impact Research;

Boers,  Niklas
External Organizations;

/persons/resource/Juergen.Kurths

Kurths,  Jürgen
Potsdam Institute for Climate Impact Research;

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Citation

Gelbrecht, M., Boers, N., Kurths, J. (2021 online): Neural Partial Differential Equations for Chaotic Systems. - New Journal of Physics.
https://doi.org/10.1088/1367-2630/abeb90


Cite as: https://publications.pik-potsdam.de/pubman/item/item_25408
Abstract
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.