12 citations · 15 across the 5 of their papers we have counts for
5 papers
Optimized Dynamic Mode Decomposition for Reconstruction and Forecasting of Atmospheric Chemistry Data
Meghana Velegar, Christoph Keller, J. Nathan Kutz
We introduce the optimized dynamic mode decomposition algorithm for constructing an adaptive and computationally efficient reduced order model and forecasting tool for global atmos…
PyDMD: A Python package for robust dynamic mode decomposition
Sara M. Ichinaga, Francesco Andreuzzi, Nicola Demo +5
The dynamic mode decomposition (DMD) is a simple and powerful data-driven modeling technique that is capable of revealing coherent spatiotemporal patterns from data. The method's l…
Dynamic Mode Decomposition for data-driven analysis and reduced-order modelling of ExB plasmas: I. Extraction of spatiotemporally coherent patterns
Farbod Faraji, Maryam Reza, Aaron Knoll +1
In this two-part article, we evaluate the utility and the generalizability of the Dynamic Mode Decomposition (DMD) algorithm for data-driven analysis and reduced-order modelling of…
SINDy with Control: A Tutorial
Urban Fasel, Eurika Kaiser, J. Nathan Kutz +2
Many dynamical systems of interest are nonlinear, with examples in turbulence, epidemiology, neuroscience, and finance, making them difficult to control using linear approaches. Mo…
Sparsity enabled cluster reduced-order models for control
Eurika Kaiser, Marek Morzynski, Guillaume Daviller +3
Characterizing and controlling nonlinear, multi-scale phenomena play important roles in science and engineering. Cluster-based reduced-order modeling (CROM) was introduced to explo…