Sparsity-promoting dynamic mode decomposition

作者：Jovanovic, Mihailo R.^*; Schmid, Peter J.; Nichols, Joseph W.

来源：Physics of Fluids, 2014, 26(2): 024103.

DOI：10.1063/1.4863670

• SPECTRAL PROPERTIES
• TUBE BUNDLE
• FLOW
• SIMULATION
• REDUCTION
• STABILITY
• SYSTEMS
• RANS
• LES

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 l(1)-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.

• 出版日期2014-2