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Communication Dans Un Congrès Année : 2018

Bilinear residual Neural Network for the identification and forecasting of dynamical systems

Résumé

Due to the increasing availability of large-scale observation and simulation datasets, data-driven representations arise as efficient and relevant computation representations of dynamical systems for a wide range of applications, where model-driven models based on ordinary differential equation remain the state-of-the-art approaches. In this work, we investigate neural networks (NN) as physically-sound data-driven representations of such systems. Reinterpreting Runge-Kutta methods as graphical models, we consider a residual NN architecture and introduce bilinear layers to embed non-linearities which are intrinsic features of dynamical systems. From numerical experiments for classic dynamical systems, we demonstrate the relevance of the proposed NN-based architecture both in terms of forecasting performance and model identification.

Dates et versions

hal-01686766 , version 1 (17-01-2018)

Identifiants

Citer

Ronan Fablet, Said Ouala, Cédric Herzet. Bilinear residual Neural Network for the identification and forecasting of dynamical systems. EUSIPCO 2018 : European Signal Processing Conference, Sep 2018, Rome, Italy. pp.1-5, ⟨10.23919/EUSIPCO.2018.8553492⟩. ⟨hal-01686766⟩
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