How can the interpretability and scalability of DMD be improved for tackling increasingly complex scientific problems?

Dynamic Mode Decomposition (DMD) is a popular data-driven analysis technique used to decompose complex, nonlinear systems into a set of modes, revealing ... A Dynamical Model for CSI Feedback in Mobile MIMO Systems using Dynamic Mode Decomposition.pdf · 516 KB · BibTEX. Last change on 16.08.2024 by F. Haddad · AIT ... Aug 20, 2019 ... Dynamic Mode Decomposition [DMD] - Wrapper ... Wrapper function to perform DMD in N-Dimensional data sets. Reshapes data back and forth to ... Feb 6, 2015 ... ... dynamic mode decomposition of large datasets. The dymode package also includes dymodem, a Matlab implementation of the code which accepts ... Abstract. This work develops compressed sensing strategies for computing the dynamic mode decomposition (DMD) from heavily subsampled or compressed data. The ... Sep 6, 2024 ... Dynamic mode decomposition analysis of very low frequency oscillations on the Irish grid. In 59th International Universities Power ... Jan 27, 2022 ... an equation-free, data-driven method for identifying coherent flow structures and their associated spatiotemporal dynamics — to estimate future ... PyDMD is a Python package that implements DMD and several of its major optimizations and methodological extensions. Dynamic mode decomposition (DMD) is a dimensionality reduction algorithm developed by Peter J. Schmid and Joern Sesterhenn in 2008. In this paper, we will propose an extension of DMD that exploits low-rank tensor decompositions of potentially high-dimensional data sets.