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Compressive sampling and dynamic mode decomposition
Steven L. Brunton, Joshua L. Proctor, J. Nathan Kutz
This work develops compressive sampling strategies for computing the dynamic mode decomposition (DMD) from heavily subsampled or output-projected data. The resulting DMD eigenvalue…
On Dynamic Mode Decomposition: Theory and Applications
Jonathan H. Tu, Clarence W. Rowley, Dirk M. Luchtenburg +2
Originally introduced in the fluid mechanics community, dynamic mode decomposition (DMD) has emerged as a powerful tool for analyzing the dynamics of nonlinear systems. However, ex…
Optimal Sensor Placement and Enhanced Sparsity for Classification
B. W. Brunton, S. L. Brunton, J. L. Proctor +1
The goal of compressive sensing is efficient reconstruction of data from few measurements, sometimes leading to a categorical decision. If only classification is required, reconstr…