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Temporal Forward-Backward Consistency, Not Residual Error, Measures the Prediction Accuracy of Extended Dynamic Mode Decomposition
Extended Dynamic Mode Decomposition (EDMD) is a popular data-driven method to approximate the action of the Koopman operator on a linear function space spanned by a dictionary of functions.
Generalizing Dynamic Mode Decomposition: Balancing Accuracy and Expressiveness in Koopman Approximations
This paper tackles the data-driven approximation of unknown dynamical systems using Koopman-operator methods.
Parallel Learning of Koopman Eigenfunctions and Invariant Subspaces For Accurate Long-Term Prediction
We identify conditions on the network topology to ensure the algorithm identifies the maximal Koopman-invariant subspace in the span of the original dictionary, characterize its time, computational, and communication complexity, and establish its robustness against communication failures.
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