Discrete-time systems with countably many but more than one omega-limit sets admit no continuous one-to-one immersion into finite-dimensional linear systems.
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Two deep learning autoregressive models predict the evolution of 2D ideal MHD instabilities while preserving key physical invariants such as global conservation trends and Alfvénic fluctuations.
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On the Nonexistence of Continuous Immersions for Discrete-time Systems
Discrete-time systems with countably many but more than one omega-limit sets admit no continuous one-to-one immersion into finite-dimensional linear systems.
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Autoregressive prediction of 2D MHD dynamics inferred from deep learning modeling
Two deep learning autoregressive models predict the evolution of 2D ideal MHD instabilities while preserving key physical invariants such as global conservation trends and Alfvénic fluctuations.