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Conference papers

Generalized Kernel-Based Dynamic Mode Decomposition

Patrick Héas 1, 2 Cédric Herzet 2 Benoit Combes 3
2 SIMSMART - SIMulation pARTiculaire de Modèles Stochastiques
IRMAR - Institut de Recherche Mathématique de Rennes, Inria Rennes – Bretagne Atlantique
3 Empenn
INSERM - Institut National de la Santé et de la Recherche Médicale, Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : Reduced modeling in high-dimensional reproducing kernel Hilbert spaces offers the opportunity to approximate efficiently non-linear dynamics. In this work, we devise an algorithm based on low rank constraint optimization and kernel-based computation that generalizes a recent approach called "kernel-based dynamic mode decomposition". This new algorithm is characterized by a gain in approximation accuracy, as evidenced by numerical simulations, and in computational complexity.
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Submitted on : Monday, January 18, 2021 - 3:19:03 PM
Last modification on : Wednesday, January 26, 2022 - 5:42:32 PM

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Patrick Héas, Cédric Herzet, Benoit Combes. Generalized Kernel-Based Dynamic Mode Decomposition. ICASSP 2020 - IEEE International Conference on Acoustics, Speech and Signal Processing, May 2020, Barcelona, Spain. pp.3877-3881, ⟨10.1109/ICASSP40776.2020.9054594⟩. ⟨hal-03113709⟩



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