What are the challenges of quantifying uncertainty in DMD-based predictions?

Jul 25, 2016 ... The DMD converts a dynamical system into a superposition of modes whose dynamics are governed by eigenvalues. Dynamic Mode Decomposition (DMD) is a popular data-driven analysis technique used to decompose complex, nonlinear systems into a set of modes, revealing ... 1. Identification of a reduced order model (ROM) from numerical or experimental data is a challenging topic in fluid dynamics. Many efforts were directed ... Jun 12, 2018 ... In this video, we introduce the dynamic mode decomposition (DMD), a recent technique to extract spatio-temporal coherent structures directly ... Feb 13, 2025 ... Based on simple least-square regression, the underlying dynamic mode decomposition algorithm can be used to construct a low-rank subspace ... The recently developed dynamic mode decomposition (DMD) is an innovative tool for integrating data with dynamical systems theory. The DMD has deep connections ... Feb 6, 2015 ... ... dynamic mode decomposition of large datasets. The dymode package also includes dymodem, a Matlab implementation of the code which accepts ... In this paper, we propose Bayesian DMD, which provides a principled way to transfer the advantages of the Bayesian formulation into DMD. To this end, we first ... Jun 12, 2018 ... In this video, we code up the dynamic mode decomposition (DMD) in Matlab and use it to analyze the fluid flow past a circular cylinder at ... We propose a novel change-point detection method based on online Dynamic Mode Decomposition with control (ODMDwC). Leveraging ODMDwC's ability to find and ...