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Nicholas Geneva
Nicholas Geneva
Senior Software Engineer HPC/AI at NVIDIA
Verified email at nvidia.com - Homepage
Title
Cited by
Cited by
Year
Modeling the dynamics of PDE systems with physics-constrained deep auto-regressive networks
N Geneva, N Zabaras
Journal of Computational Physics 403, 109056, 2020
1602020
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
842019
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
392018
Transformers for modeling physical systems
N Geneva, N Zabaras
Neural Networks 146, 272-289, 2022
312022
Multi-fidelity generative deep learning turbulent flows
N Geneva, N Zabaras
Foundations of Data Science 2, 391, 2020
232020
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
192019
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
162017
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
102017
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
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Articles 1–11