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math.DS2026
Linear Recurrent Neural Networks as Time-Delay Embeddings
Fisher Ng, J. Nathan Kutz
Sequence models, and particularly Linear Recurrent Neural Networks (LRNNs) of the form , are widely applic…
math.DS2025
Phasor notation of Dynamic Mode Decomposition
Karl Lapo, Samuele Mosso, J. Nathan Kutz
Dynamic Mode Decomposition (DMD) is a powerful, data-driven method for diagnosing complex dynamics. Various DMD algorithms allow one to fit data with a low-rank model that decompos…
math.DS2025
Kernel Dynamic Mode Decomposition For Sparse Reconstruction of Closable Koopman Operators
Nishant Panda, Himanshu Singh, J. Nathan Kutz
Spatial temporal reconstruction of dynamical system is indeed a crucial problem with diverse applications ranging from climate modeling to numerous chaotic and physical processes.…