Modeling the dynamics of PDE systems with physics-constrained deep auto-regressive networks N Geneva, N Zabaras Journal of Computational Physics 403, 109056, 2020 | 160 | 2020 |

Quantifying model form uncertainty in Reynolds-averaged turbulence models with Bayesian deep neural networks N Geneva, N Zabaras Journal of Computational Physics 383, 125-147, 2019 | 84 | 2019 |

Direct numerical simulation of turbulent pipe flow using the lattice Boltzmann method C Peng, N Geneva, Z Guo, LP Wang Journal of Computational Physics 357, 16-42, 2018 | 39 | 2018 |

Transformers for modeling physical systems N Geneva, N Zabaras Neural Networks 146, 272-289, 2022 | 31 | 2022 |

Multi-fidelity generative deep learning turbulent flows N Geneva, N Zabaras Foundations of Data Science 2, 391, 2020 | 23 | 2020 |

A lattice-Boltzmann scheme of the Navier–Stokes equation on a three-dimensional cuboid lattice LP Wang, H Min, C Peng, N Geneva, Z Guo Computers & Mathematics with Applications 78 (4), 1053-1075, 2019 | 19 | 2019 |

A scalable interface-resolved simulation of particle-laden flow using the lattice Boltzmann method N Geneva, C Peng, X Li, LP Wang Parallel Computing 67, 20-37, 2017 | 16 | 2017 |

Issues associated with Galilean invariance on a moving solid boundary in the lattice Boltzmann method C Peng, N Geneva, Z Guo, LP Wang Physical Review E 95 (1), 013301, 2017 | 10 | 2017 |

Investigation of turbulence modulation in particle-laden flows using the lattice Boltzmann method. C Peng, N Geneva, H Min, LP Wang APS Division of Fluid Dynamics Meeting Abstracts, A4. 001, 2015 | | 2015 |

Different Scalable Implementations of Collision and Streaming for Optimal Computational Performance of Lattice Boltzmann Simulations N Geneva, LP Wang APS Division of Fluid Dynamics Meeting Abstracts, G6. 008, 2015 | | 2015 |

Modern Deep Learning for Modeling Physical Systems N Geneva, N Zabaras Knowledge-Guided Machine Learning, 161-178, 0 | | |