What are the limitations of applying DMD to complex biological data?

In this paper, we will propose an extension of DMD that exploits low-rank tensor decompositions of potentially high-dimensional data sets. Mar 1, 2023 ... Abstract. In this work, we demonstrate how physical principles—such as symmetries, invariances and conservation laws—can be integrated into the ... Jun 12, 2018 ... In this video, we introduce the dynamic mode decomposition (DMD), a recent technique to extract spatio-temporal coherent structures directly ... Oct 5, 2024 ... Keyword(s): Dynamic Mode Decomposition, Flow modeling and control, Convex optimization, Sparsity. [bibtex-entry]. Conference articles. E ... Nov 4, 2014 ... Dynamic mode decomposition (DMD) is a data-driven computational technique capable of extracting dynamical information from flowfields measured ... Nov 12, 2021 ... The method is capable of characterizing nonlinear, time-varying, multidimensional time series data in a computationally efficient manner. The ... Aug 29, 2024 ... We apply here an unsupervised data-driven algorithm, Dynamic Mode Decomposition (DMD), to sEEG recordings from five focal epilepsy patients. Feb 5, 2021 ... This was coupled with my discovery for Koopman Operator and Dynamic Mode Decomposition which got me thinking about how collecting data on ... In particular, we explore an algorithm rooted in fluid-dynamics, known as higher-order Dynamic Mode Decomposition, which is designed to capture the ... The recently developed dynamic mode decomposition (DMD) is an innovative tool for integrating data with dynamical systems theory. The DMD has deep connections ...