Enhancing Dynamic-Mode Decomposition via Multi-Scale Analysis

Published 9 Jan 2020 in math.DS | (2001.02795v1)

Abstract: Through the use of wavelet based Besov norms, we compute nontrivial multiscale nonlinear features of a given data set so as to enhance the standard Dynamic-Mode Decomposition algorithm. Thus we are able to build sophisticated observables which enhance algorithm performance without placing undue computational burdens on the user.

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Authors (1)

Christopher W. Curtis

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