Compressive sampling and dynamic mode decomposition

Understand

This work develops compressive sampling strategies for computing the dynamic mode decomposition (DMD) from heavily subsampled or output-projected data.

• The resulting DMD eigenvalues are equal to DMD eigenvalues from the full-state data.
• It is then possible to reconstruct full-state DMD eigenvectors using $\ell_1$-minimization or greedy algorithms.
• If full-state snapshots are available, it may be computationally beneficial to compress the data, compute a compressed DMD, and then reconstruct full-state modes by applying the projected DMD transforms to full-state snapshots.