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20192026
most citedLagrangian Dynamic Mode Decomposition for Construction of Reduced-Order Models of Advection-Dominated Phenomena

71 citations · 141 across the 11 of their papers we have counts for

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7 papers · 1 filter

math.NA2026

Structure-Informed Data-Driven Reduced-Order Modeling of Scalar Hyperbolic Conservation Laws via Kinetic Defect Measure

Marissa Llamas, Jan Fuhg, Hannah Lu

Reduced-order modeling of transport-dominated systems remains challenging because moving fronts and shocks are poorly represented by low-dimensional linear subspaces. We develop a…

math.NA2023★ 1 cited

Learning Nonautonomous Systems via Dynamic Mode Decomposition

Hannah Lu, Daniel M. Tartakovsky

We present a data-driven learning approach for unknown nonautonomous dynamical systems with time-dependent inputs based on dynamic mode decomposition (DMD). To circumvent the diffi…

math.NA2022★ 2 cited

Model Reduction via Dynamic Mode Decomposition

Hannah Lu, Daniel M. Tartakovsky

This work proposes a new framework of model reduction for parametric complex systems. The framework employs a popular model reduction technique dynamic mode decomposition (DMD), wh…

math.NA2020★ 28 cited

Extended Dynamic Mode Decomposition for Inhomogeneous Problems

Hannah Lu, Daniel M. Tartakovsky

Dynamic mode decomposition (DMD) is a powerful data-driven technique for construction of reduced-order models of complex dynamical systems. Multiple numerical tests have demonstrat…

math.NA2020★ 2 cited

Dynamic Mode Decomposition for Construction of Reduced-Order Models of Hyperbolic Problems with Shocks

Hannah Lu, Daniel M. Tartakovsky

Construction of reduced-order models (ROMs) for hyperbolic conservation laws is notoriously challenging mainly due to the translational property and nonlinearity of the governing e…

math.NA2019★ 71 cited

Lagrangian Dynamic Mode Decomposition for Construction of Reduced-Order Models of Advection-Dominated Phenomena

Hannah Lu, Daniel M. Tartakovsky

Proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD) are two complementary singular-value decomposition (SVD) techniques that are widely used to construct red…

math.NA2019★ 10 cited

Predictive Accuracy of Dynamic Mode Decomposition

Hannah Lu, Daniel M. Tartakovsky

Dynamic mode decomposition (DMD), which the family of singular-value decompositions (SVD), is a popular tool of data-driven regression. While multiple numerical tests demonstrated…