Abstract:
When considering the data-driven identification of non-linear differential equations, the choice of the integration scheme to use is far from being trivial and may dramat...Show MoreMetadata
Abstract:
When considering the data-driven identification of non-linear differential equations, the choice of the integration scheme to use is far from being trivial and may dramatically impact the identification problem. In this work, we discuss this aspect and propose a novel architecture that jointly learns Neural Ordinary Differential Equations (NODEs) as well as the corresponding integration schemes that would minimize the forecast of a given sequence of observations. We demonstrate its relevance with numerical experiments on non-linear dynamics, including chaotic systems.
Published in: ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Date of Conference: 14-19 April 2024
Date Added to IEEE Xplore: 18 March 2024
ISBN Information: