A wavelet-based dynamic mode decomposition for modeling mechanical systems from partial observations

Dynamic mode decomposition (DMD) has emerged as a popular data-driven modeling approach to identifying spatio-temporal coherent structures in dynamical systems, owing to its strong relation with the Koopman operator. For dynamical systems with external forcing, the iden-tified model should not only be suitable for a specific forcing function but should generally approximate the input-output behavior of the underlying dynamics. A novel methodology for modeling those classes of dynamical systems is proposed in the present work, using wavelets in conjunction with the input-output dynamic mode decomposition (ioDMD). The wavelet-based dynamic mode decomposition (WDMD) builds on the ioDMD framework without the restrictive assumption of full state measurements. Our non-intrusive approach constructs numerical models directly from trajectories of the full model's inputs and outputs, without requiring the full -model operators. These trajectories are generated by running a simulation of the full model or observing the original dynamical systems' response to inputs in an experimental framework. Hence, the present methodology is applicable for dynamical systems whose internal state vector measurements are not available. Instead, data from only a few output locations are only accessible, as often the case in practice. The present methodology's applicability is explained by modeling the input-output response of an Euler-Bernoulli finite element beam model. The WDMD provides a linear state-space representation of the dynamical system using the response measurements and the corresponding input forcing functions. The developed state -space model can then be used to simulate the beam's response towards different types of forcing functions. The method is further validated on a real (experimental) data set using modal analysis on a simple free-free beam, demonstrating the efficacy of the proposed methodology as an appropriate candidate for modeling practical dynamical systems despite having no access to internal state measurements and treating the full model as a black-box.

Automated summary

Dynamic mode decomposition (DMD) is a popular data-driven modeling approach for identifying spatio-temporal coherent structures in dynamical systems. This work proposes a novel methodology, called wavelet-based dynamic mode decomposition (WDMD), which performs modeling without the restrictive assumption of full state measurements. The WDMD approach is applicable to dynamical systems where internal state vector measurements are not available.