12 citations · 17 across the 5 of their papers we have counts for
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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…
Transfer Learning on Multi-Fidelity Data
Dong H. Song, Daniel M. Tartakovsky
Neural networks (NNs) are often used as surrogates or emulators of partial differential equations (PDEs) that describe the dynamics of complex systems. A virtually negligible compu…
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…
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…
Estimation of distributions via multilevel Monte Carlo with stratified sampling
Søren Taverniers, Daniel M. Tartakovsky
We design and implement a novel algorithm for computing a multilevel Monte Carlo (MLMC) estimator of the cumulative distribution function of a quantity of interest in problems with…