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Generalized Kernel-Based Dynamic Mode Decomposition

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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.

Dates and versions

hal-03113709 , version 1 (18-01-2021)

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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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