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Boosting performance in Machine Learning of Turbulent and Geophysical Flows via scale separation

Abstract : Recent advances in statistical learning have opened the possibility to forecast the behavior of chaotic systems using recurrent neural networks. In this letter we investigate the applicability of this framework to geophysical flows, known to be intermittent and turbulent. We show that both turbulence and intermittency introduce severe limitations on the applicability of recurrent neural networks, both for short term forecasts as well as for the reconstruction of the underlying attractor. We suggest that possible strategies to overcome such limitations should be based on separating the smooth large-scale dynamics, from the intermittent/turbulent features. We test these ideas on global sea-level pressure data for the past 40 years, a proxy of the atmospheric circulation dynamics.
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https://hal.archives-ouvertes.fr/hal-02337839
Contributor : Faranda Davide <>
Submitted on : Tuesday, October 29, 2019 - 4:25:46 PM
Last modification on : Monday, April 6, 2020 - 9:17:49 AM
Document(s) archivé(s) le : Thursday, January 30, 2020 - 9:13:24 PM

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  • HAL Id : hal-02337839, version 1

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Davide Faranda, M. Vrac, P. Yiou, F. M. E. Pons, A Hamid, et al.. Boosting performance in Machine Learning of Turbulent and Geophysical Flows via scale separation. 2019. ⟨hal-02337839⟩

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