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Xuhui Meng
Xuhui Meng
School of Mathematics and Statistics, HUST
Verified email at hust.edu.cn
Title
Cited by
Cited by
Year
DeepXDE: A deep learning library for solving differential equations
L Lu, X Meng, Z Mao, GE Karniadakis
SIAM review 63 (1), 208-228, 2021
14042021
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
6042021
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
4602020
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
3732020
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
2712022
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
2612022
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
1582023
Multi-fidelity Bayesian neural networks: Algorithms and applications
X Meng, H Babaee, GE Karniadakis
Journal of Computational Physics 438, 110361, 2021
1152021
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
1122021
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
622022
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
582015
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
402015
Learning functional priors and posteriors from data and physics
X Meng, L Yang, Z Mao, J del Águila Ferrandis, GE Karniadakis
Journal of Computational Physics 457, 111073, 2022
382022
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
352016
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
302017
NeuralUQ: A comprehensive library for uncertainty quantification in neural differential equations and operators
Z Zou, X Meng, AF Psaros, GE Karniadakis
arXiv preprint arXiv:2208.11866, 2022
262022
A fast multi-fidelity method with uncertainty quantification for complex data correlations: Application to vortex-induced vibrations of marine risers
X Meng, Z Wang, D Fan, MS Triantafyllou, GE Karniadakis
Computer Methods in Applied Mechanics and Engineering 386, 114212, 2021
222021
Boundary scheme for linear heterogeneous surface reactions in the lattice Boltzmann method
X Meng, Z Guo
Physical Review E 94 (5), 053307, 2016
192016
Discrete effects on boundary conditions of the lattice Boltzmann method for fluid flows with curved no-slip walls
L Wang, S Tao, X Meng, K Zhang, G Lu
Physical Review E 101 (6), 063307, 2020
142020
Physics-informed neural networks with residual/gradient-based adaptive sampling methods for solving partial differential equations with sharp solutions
Z Mao, X Meng
Applied Mathematics and Mechanics 44 (7), 1069-1084, 2023
11*2023
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