Wavelet-based dynamic mode decomposition
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Abstract Dynamic mode decomposition (DMD) has emerged as a leading data-driven technique to identify the spatio-temporal coherent structure in dynamical systems, owing to its strong relation with the Koopman operator. For dynamical systems with external forcing, the identified model should not only be suitable for a specific forcing function but should generally approximate the input-output behavior of the data source. In this work, we propose a novel methodology, called the wavelet-based DMD (WDMD), that integrates wavelet decompositions with ioDMD to approximate dynamical systems from partial measurement data. The method is validated using a numerical and experimental case study involving modal analysis on a simple finite element model and free-free beam respectively.