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Dynamic Mode Decomposition (DMD) yields a linear, approximate model of asystem's dynamics that is built from data. We seek to reduce the order of thismodel by identifying a reduced set of modes that best fit the output. We adopta model selection algorithm from statistics and machine learning known as LeastAngle Regression (LARS). We modify LARS to be complex-valued and utilize LARSto select DMD modes. We refer to the resulting algorithm as Least AngleRegression for Dynamic Mode Decomposition (LARS4DMD). Sparsity-PromotingDynamic Mode Decomposition (DMDSP), a popular mode-selection algorithm, servesas a benchmark for comparison. Numerical results from a Poiseuille flow testproblem show that LARS4DMD yields reduced-order models that have comparableperformance to DMDSP. LARS4DMD has the added benefit that the regularizationweighting parameter required for DMDSP is not needed.

Reduced-order modeling using Dynamic Mode Decomposition and Least Angle Regression