Artikel mit Open-Access-Mandaten - Xuhui MengWeitere Informationen
Nicht verfügbar: 9
A localized mass-conserving lattice Boltzmann approach for non-Newtonian fluid flows
L Wang, J Mi, X Meng, Z Guo
Communications in Computational Physics 17 (4), 908-924, 2015
Mandate: National Natural Science Foundation of China
Localized lattice Boltzmann equation model for simulating miscible viscous displacement in porous media
X Meng, Z Guo
International Journal of Heat and Mass Transfer 100, 767-778, 2016
Mandate: National Natural Science Foundation of China
Pore-scale study on reactive mixing of miscible solutions with viscous fingering in porous media
T Lei, X Meng, Z Guo
Computers & Fluids 155, 146-160, 2017
Mandate: National Natural Science Foundation of China
Boundary scheme for linear heterogeneous surface reactions in the lattice Boltzmann method
X Meng, Z Guo
Physical Review E 94 (5), 053307, 2016
Mandate: National Natural Science Foundation of China
Preconditioned multiple-relaxation-time lattice Boltzmann equation model for incompressible flow in porous media
X Meng, L Wang, X Yang, Z Guo
Physical Review E 98 (5), 053309, 2018
Mandate: National Natural Science Foundation of China
Simulating flow in porous media using the lattice Boltzmann method: Intercomparison of single-node boundary schemes from benchmarking to application
X Meng, L Wang, W Zhao, X Yang
Advances in Water Resources 141, 103583, 2020
Mandate: National Natural Science Foundation of China
Uncertainty quantification on the macroscopic properties of heterogeneous porous media
P Wang, H Chen, X Meng, X Jiang, D Xiu, X Yang
Physical Review E 98 (3), 033306, 2018
Mandate: National Natural Science Foundation of China
A multiscale study of density-driven flow with dissolution in porous media
X Meng, H Sun, Z Guo, X Yang
Advances in Water Resources 142, 103640, 2020
Mandate: National Natural Science Foundation of China
Discrete effect on single-node boundary schemes of lattice Bhatnagar–Gross–Krook model for convection-diffusion equations
L Wang, X Meng, HC Wu, TH Wang, G Lu
International Journal of Modern Physics C 31 (01), 2050017, 2020
Mandate: National Natural Science Foundation of China
Verfügbar: 18
DeepXDE: A deep learning library for solving differential equations
L Lu, X Meng, Z Mao, GE Karniadakis
SIAM review 63 (1), 208-228, 2021
Mandate: US Department of Energy, US Department of Defense
B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data
L Yang, X Meng, GE Karniadakis
Journal of Computational Physics 425, 109913, 2021
Mandate: US Department of Energy, US Department of Defense, US National Institutes of …
A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse PDE problems
X Meng, GE Karniadakis
Journal of Computational Physics 401, 109020, 2020
Mandate: US Department of Energy, US Department of Defense
PPINN: Parareal physics-informed neural network for time-dependent PDEs
X Meng, Z Li, D Zhang, GE Karniadakis
Computer Methods in Applied Mechanics and Engineering 370, 113250, 2020
Mandate: US Department of Energy
Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems
J Yu, L Lu, X Meng, GE Karniadakis
Computer Methods in Applied Mechanics and Engineering 393, 114823, 2022
Mandate: US Department of Energy, US Department of Defense
A comprehensive and fair comparison of two neural operators (with practical extensions) based on fair data
L Lu, X Meng, S Cai, Z Mao, S Goswami, Z Zhang, GE Karniadakis
Computer Methods in Applied Mechanics and Engineering 393, 114778, 2022
Mandate: US Department of Energy, US Department of Defense
Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons
AF Psaros, X Meng, Z Zou, L Guo, GE Karniadakis
Journal of Computational Physics 477, 111902, 2023
Mandate: US Department of Energy, US Department of Defense
Physics-informed neural networks for solving forward and inverse flow problems via the Boltzmann-BGK formulation
Q Lou, X Meng, GE Karniadakis
Journal of Computational Physics 447, 110676, 2021
Mandate: US Department of Energy, National Natural Science Foundation of China
Multi-fidelity Bayesian neural networks: Algorithms and applications
X Meng, H Babaee, GE Karniadakis
Journal of Computational Physics 438, 110361, 2021
Mandate: US Department of Energy, US Department of Defense, US National Institutes of …
Bayesian Physics Informed Neural Networks for real-world nonlinear dynamical systems
K Linka, A Schäfer, X Meng, Z Zou, GE Karniadakis, E Kuhl
Computer Methods in Applied Mechanics and Engineering 402, 115346, 2022
Mandate: US Department of Defense
Multiple-relaxation-time lattice Boltzmann model for incompressible miscible flow with large viscosity ratio and high Péclet number
X Meng, Z Guo
Physical Review E 92 (4), 043305, 2015
Mandate: National Natural Science Foundation of China
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