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Recurrent neural networks and Koopman-based frameworks for temporal predictions in a low-order model of turbulence |
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| Affiliation: | 1. Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran;2. SimEx/FLOW, Engineering Mechanics, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden;3. Swedish e-Science Research Centre (SeRC), Stockholm, Sweden;4. Division of Robotics, Perception, and Learning, School of EECS, KTH Royal Institute of Technology, Stockholm, Sweden |
| Abstract: |  |
| Keywords: | Dynamical systems Machine learning Data-driven modeling Recurrent neural networks Koopman operator |
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