Publications

[14].  H. Lu and D. Tartakovsky, “Data-driven models of nonautonomous systems”, Journal of Computational Physics (2024): 112976, doi: https://doi.org/10.1016/j.jcp.2024.112976

[13].  H. Lu and D. Tartakovsky, “DRIPS: A framework of Dimension Reduction and Interpolation in Parameter Space”, Journal of Computational Physics (2023): 112455, doi: https://doi.org/10.1016/j.jcp.2023.112455

[12].  H. Lu, G. Francesco and D. Tartakovsky, “Parsimonious models of in-host viraldynamics and immune response”, Applied Mathematics Letters 145 (2023): 108781, doi: https://doi.org/10.1016/j.aml.2023.108781

[11].  H. Lu, D. Ermakova, H. Wainwright, L. Zheng and D. Tartakovsky, “Data-driven Emulators for Multi-physics Phenomena in Porous Media”, Journal of Machine Learning for Modeling and Computing 2.2 (2021), doi: http://dx.doi.org/10.1615/JMachLearnModelComput.2021038577

[10].  H. Lu and D. Tartakovsky, “Extended Dynamic Mode Decomposition for Inhomogeneous Problems”, Journal of Computational Physics (2021): 110550, doi: https://doi.org/10.1016/j.jcp.2021. 110550

[9].  D. Ermakova, H. Wainwright, L. Zheng, I. Shirley and H. Lu, “Global (VI) integrating coupled THC into PA model”, Journal of Nuclear Engineering and Radiation Science 7.4 (2021): 041902, doi: https://doi.org/10.1115/1.4050297

[8].  H. Lu, Kimoon Um and D. Tartakovsky, “Hybrid Models of Chemotaxis with Application to Leukocyte Migration”, Journal of Mathematical Biology 82.4 (2021): 1-28, doi: http://dx.doi.org/10. 1007/s00285-021-01581-7

[7].  H. Lu and D. Tartakovsky, “Dynamic Mode Decomposition for Construction of Reduced-Order Models of Hyperbolic Problems with Shocks”, Journal of Machine Learning for Modeling and Computing 2.1 (2021), doi: http://dx.doi.org/10.1615/JMachLearnModelComput.2021036132

[6].  H. Lu, C. Weintz, J. Pace, D. Indana, K. Linka and E. Kuhl, “Are college campuses superspreaders? A data-driven modeling study.”, Computer Methods in Biomechanics and Biomedical Engineering (2020): 1-11, doi: https://doi.org/10.1080/10255842.2020.1869221

[5].  H. Lu and D. Tartakovsky, “Prediction Accuracy Analysis for Dynamic Mode Decomposition”, SIAM Journal on Scientific Computing 42.3 (2020): A1639-A1662, doi: https://doi.org/10.1137/ 19M1259948

[4].  H. Lu and D. Tartakovsky, “Lagrangian Dynamic Mode Decomposition for Construction of Reduced-Order Models of Advection-Dominated Phenomena”, Journal of Computational Physics 407 (2020): 109229, doi: https://doi.org/10.1016/j.jcp.2020.109229

[3].  S. Jin, H. Lu* and L. Pareschi, “A High Order Stochastic Asymptotic Preserving Scheme for Chemotaxis Kinetic Models with Random Inputs”, Multiscale Modeling & Simulation 16.4 (2018): 1884- 1915, doi: https://doi.org/10.1137/17M1150840

[2].  S. Jin, H. Lu* and L. Pareschi, “Efficient Stochastic Asymptotic-Preserving IMEX Methods for Transport Equations with Diffusive Scalings and Random Inputs”, SIAM Journal on Scientific Computing 40.2 (2018): A671-A696, doi: https://doi.org/10.1137/17M1120518

[1].  S. Jin and H. Lu*, “An Asymptotic-Preserving Stochastic Galerkin Method for the Radiative Heat Transfer Equations with Random Inputs and Diffusive Scalings”, Journal of Computational Physics 334 (2017): 182-206, doi: https://doi.org/10.1016/j.jcp.2016.12.033

[17].  H. Lu, L. Sal ́o-Salgado, Y. Marzouk and R. Juanes, “ Uncertainty Quantification of CO2 Leakage and Risk Analysis of Induced Seismicity for Large-sclae Geological CO2 Sequestration”, submitted to Water Resources Research (2023)

[16].  M. D’Elia, ..., H. Lu, et al. “Machine Learning in Heterogeneous Porous Materials”, (2022), arXiv: https://arxiv.org/abs/2202.04137

[15].  L. Jin, H. Lu* and G. Wen, “Fast uncertainty quantification of reservoir simulation with variational U-Net”, (2019), arXiv: https://arxiv.org/abs/1907.00718

[21].  H. Lu, S. Silvestri, S. Bishnu, R. Ferrari and Y. Marzouk, “Digital Twins of Ocean Responses to Climate Change”, (2023)

[20]. H. Lu, L. Sal ́o-Salgado, E. Haghighat and R. Juanes, “Scientific Machine Learning for Surrogate Modeling, Parameter Identification and Transfer Learning of Multiphase Flows in Porous Media: Application to the FluidFlower CO2 Injection Experiment”, (2023), 

[19].  R. Gentles, L. Sal ́o-Salgado, H. Lu and R. Juanes, “Feature-based Clustering of Seismograms from Geological Carbon Dioxide Sequestration Site”, (2023), (Gentles et al., 2023).

[18].  H. Lu and D. Tartakovsky, “Data-driven models of partially-observed systems”, (2023), (Lu et al., 2023g).

“Reduced-Order Models of Transport Phenomena”, Stanford University (2022).