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- W2906206094 abstract "We show the skills of a data-driven low-dimensional linear model in predicting the spatio-temporal evolution of turbulent Rayleigh-Benard convection. The model is based on dynamic mode decomposition with delay-embedding, which provides a data-driven finite-dimensional approximation to the system's Koopman operator. The model is built using vector-valued observables from direct numerical simulations, and can provide accurate predictions. Similar high prediction skills are found for the Kuramoto-Sivashinsky equation in the strongly-chaotic regimes." @default.
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- W2906206094 date "2018-12-21" @default.
- W2906206094 modified "2023-09-27" @default.
- W2906206094 title "Data-driven Spatio-temporal Prediction of High-dimensional Geophysical Turbulence using Koopman Operator Approximation" @default.
- W2906206094 hasPublicationYear "2018" @default.
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