Temporally-consistent koopman autoencoders for forecasting dynamical systems
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DOI: 10.1038/s41598-025-05222-7
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,… read more here.
Keywords: temporally consistent; dynamical systems; koopman autoencoders; consistent koopman ... See more keywords