71 citations · 141 across the 11 of their papers we have counts for
12 papers
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…
Data-Driven Modeling and Correction of Vehicle Dynamics
Nguyen Ly, Caroline Tatsuoka, Jai Nagaraj +4
We develop a data-driven framework for learning and correcting non-autonomous vehicle dynamics. Physics-based vehicle models are often simplified for tractability and therefore exh…
Efficient data-driven regression for reduced-order modeling of spatial pattern formation
Alessandro Alla, Rudy Geelen, Hannah Lu
We present an efficient data-driven regression approach for constructing reduced-order models (ROMs) of reaction-diffusion systems exhibiting pattern formation. The ROMs are learne…
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…
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…
Machine Learning in Heterogeneous Porous Materials
Marta D'Elia, Hang Deng, Cedric Fraces +21
The "Workshop on Machine learning in heterogeneous porous materials" brought together international scientific communities of applied mathematics, porous media, and material scienc…
Data-informed Emulators for Multi-Physics Simulations
Hannah Lu, Dinara Ermakova, Haruko Murakami Wainwright +2
Machine learning techniques are powerful tools for construction of emulators for complex systems. We explore different machine learning methods and conceptual methodologies, rangin…
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…
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…
Fast uncertainty quantification of reservoir simulation with variational U-Net
Larry Jin, Hannah Lu, Gege Wen
Quantification of uncertainty in production/injection forecasting is an important aspect of reservoir simulation studies. Conventional approaches include intrusive Galerkin-based m…
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…