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Temporally-Consistent Koopman Autoencoders for Forecasting Dynamical Systems
March 20, 2024, 4:41 a.m. | Indranil Nayak, Debdipta Goswami, Mrinal Kumar, Fernando Teixeira
cs.LG updates on arXiv.org arxiv.org
Abstract: Absence of sufficiently high-quality data often poses a key challenge in data-driven modeling of high-dimensional spatio-temporal dynamical systems. Koopman Autoencoders (KAEs) harness the expressivity of deep neural networks (DNNs), the dimension reduction capabilities of autoencoders, and the spectral properties of the Koopman operator to learn a reduced-order feature space with simpler, linear dynamics. However, the effectiveness of KAEs is hindered by limited and noisy training datasets, leading to poor generalizability. To address this, we introduce …
abstract arxiv autoencoders capabilities challenge consistent cs.lg data data-driven feature forecasting harness key learn modeling networks neural networks quality quality data systems temporal type
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