7 citations · 8 across the 2 of their papers we have counts for
5 papers
Centering Data Improves the Dynamic Mode Decomposition
Seth M. Hirsh, Kameron Decker Harris, J. Nathan Kutz +1
Dynamic mode decomposition (DMD) is a data-driven method that models high-dimensional time series as a sum of spatiotemporal modes, where the temporal modes are constrained by line…
Dynamic Mode Decomposition and Sparse Measurements for Characterization and Monitoring of Power System Disturbances
J. Jorge Ramos, J. Nathan Kutz
We introduce the dynamics mode decomposition for monitoring wide-area power grid networks from sparse measurement data. The mathematical framework fuses data from multiple sensors…
Robust and scalable methods for the dynamic mode decomposition
Travis Askham, Peng Zheng, Aleksandr Aravkin +1
The dynamic mode decomposition (DMD) is a broadly applicable dimensionality reduction algorithm that approximates a matrix containing time-series data by the outer product of a mat…
Inferring biological networks by sparse identification of nonlinear dynamics
Niall M. Mangan, Steven L. Brunton, Joshua L. Proctor +1
Inferring the structure and dynamics of network models is critical to understanding the functionality and control of complex systems, such as metabolic and regulatory biological ne…
Classification of Spatio-Temporal Data via Asynchronous Sparse Sampling: Application to Flow Around a Cylinder
Ido Bright, Guang Lin, J. Nathan Kutz
We present a novel method for the classification and reconstruction of time dependent, high-dimensional data using sparse measurements, and apply it to the flow around a cylinder.…