| Modeling the dynamics of PDE systems with physics-constrained deep auto-regressive networks N Geneva, N Zabaras Journal of Computational Physics 403, 109056, 2020 | 336 | 2020 |
| Transformers for modeling physical systems N Geneva, N Zabaras Neural Networks 146, 272-289, 2022 | 176 | 2022 |
| 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 | 155 | 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 | 64 | 2018 |
| Multi-fidelity generative deep learning turbulent flows N Geneva, N Zabaras Foundations of Data Science 2, 391, 2020 | 56 | 2020 |
| ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation S Yu, W Hannah, L Peng, J Lin, MA Bhouri, R Gupta, B Lütjens, JC Will, ... Advances in Neural Information Processing Systems 36, 22070-22084, 2023 | 25 | 2023 |
| 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 | 23 | 2017 |
| 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 | 20 | 2019 |
| 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 | 12 | 2017 |
| Modern Deep Learning for Modeling Physical Systems N Geneva, N Zabaras Knowledge Guided Machine Learning, 161-178, 2022 | | 2022 |
| 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 |
Nicholas ZabarasProfessor, Cornell UniversityПодтвержден адрес электронной почты в домене nd.edu
