Sparsity-promoting dynamic mode decomposition

Mihailo R. Jovanović, Peter J. Schmid, Joseph W. Nichols

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490 Scopus citations

Abstract

Dynamic mode decomposition (DMD) represents an effective means for capturing the essential features of numerically or experimentally generated flow fields. In order to achieve a desirable tradeoff between the quality of approximation and the number of modes that are used to approximate the given fields, we develop a sparsity-promoting variant of the standard DMD algorithm. Sparsity is induced by regularizing the least-squares deviation between the matrix of snapshots and the linear combination of DMD modes with an additional term that penalizes the l1-norm of the vector of DMD amplitudes. The globally optimal solution of the resulting regularized convex optimization problem is computed using the alternating direction method of multipliers, an algorithm well-suited for large problems. Several examples of flow fields resulting from numerical simulations and physical experiments are used to illustrate the effectiveness of the developed method. © 2014 AIP Publishing LLC.
Original languageEnglish (US)
JournalPhysics of Fluids
Volume26
Issue number2
DOIs
StatePublished - Feb 1 2014
Externally publishedYes

ASJC Scopus subject areas

  • Condensed Matter Physics

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