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RESIDUAL INTEGRATION NEURAL NETWORK

Said Ouala 1 Ananda Pascual 2 Ronan Fablet 3
3 Lab-STICC_TB_CID_TOMS
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
Abstract : In this work, we investigate residual neural network representations for the identification and forecasting of dynamical systems. We propose a novel architecture that jointly learns the dynamical model and the associated Runge-Kutta integration scheme. We demonstrate the relevance of the proposed architecture with respect to learning-based state-of-the-art approaches in the identification and forecasting of chaotic dynamics when provided with training data with low temporal sampling rates.
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https://hal-imt-atlantique.archives-ouvertes.fr/hal-02005399
Contributor : Said Ouala <>
Submitted on : Monday, February 4, 2019 - 9:52:13 AM
Last modification on : Wednesday, June 24, 2020 - 4:19:39 PM
Long-term archiving on: : Sunday, May 5, 2019 - 12:55:14 PM

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  • HAL Id : hal-02005399, version 1

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Said Ouala, Ananda Pascual, Ronan Fablet. RESIDUAL INTEGRATION NEURAL NETWORK. 2019. ⟨hal-02005399⟩

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