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Residual Integration Neural Network

Said Ouala 1 Ananda Pascual 2 Ronan Fablet 1, 3
3 Lab-STICC_IMTA_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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Submitted on : Monday, February 4, 2019 - 9:52:13 AM
Last modification on : Saturday, May 1, 2021 - 3:54:11 AM
Long-term archiving on: : Sunday, May 5, 2019 - 12:55:14 PM

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Said Ouala, Ananda Pascual, Ronan Fablet. Residual Integration Neural Network. ICASSP 2019 : IEEE International Conference on Acoustics, Speech and Signal Processing, May 2019, Brighton, United Kingdom. ⟨10.1109/ICASSP.2019.8683447⟩. ⟨hal-02005399⟩

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