Dynamic Mode Decomposition for Construction of Reduced-Order Models of Hyperbolic Problems with Shocks. - Hannah Lu; Daniel M. Tartakovsky (CoRR, 2020)

Lagrangian dynamic mode decomposition for construction of reduced-order models of advection-dominated phenomena. - Hannah Lu; Daniel M. Tartakovsky (J. Comput. Phys., 2020)

Prediction Accuracy of Dynamic Mode Decomposition. - Hannah Lu; Daniel M. Tartakovsky (SIAM J. Sci. Comput., 2020)

Extended dynamic mode decomposition for inhomogeneous problems. - Hannah Lu; Daniel M. Tartakovsky (J. Comput. Phys., 2021)

Machine Learning in Heterogeneous Porous Materials. - Marta D'Elia; Hang Deng; Cedric G. Fraces; Krishna C. Garikipati; Lori Graham-Brady; Amanda A. Howard; George Em Karniadakis; Vahid Keshavarzzadeh; Robert M. Kirby; J. Nathan Kutz; Chunhui Li; Xing Liu; et al. (CoRR, 2022)

Model Reduction via Dynamic Mode Decomposition. - Hannah Lu; Daniel M. Tartakovsky (CoRR, 2022)

Learning Nonautonomous Systems via Dynamic Mode Decomposition. - Hannah Lu; Daniel M. Tartakovsky (CoRR, 2023)

Parsimonious models of in-host viral dynamics and immune response. - Hannah Lu; Francesco Giannino; Daniel M. Tartakovsky (Appl. Math. Lett., 2023)

DRIPS: A framework for dimension reduction and interpolation in parameter space. - Hannah Lu; Daniel M. Tartakovsky (J. Comput. Phys., 2023)

Data-driven models of nonautonomous systems. - Hannah Lu; Daniel M. Tartakovsky (J. Comput. Phys., 2024)