Xuhui Meng

发表

PPINN: Parareal Physics-Informed Neural Network for time-dependent PDEs pdf

George Em Karniadakis, Xuhui Meng, Zhen Li, 2019, Computer Methods in Applied Mechanics and Engineering.

A fast multi-fidelity method with uncertainty quantification for complex data correlations: Application to vortex-induced vibrations of marine risers

G. Karniadakis, M. Triantafyllou, Zhicheng Wang, 2020, Computer Methods in Applied Mechanics and Engineering.

Boundary scheme for linear heterogeneous surface reactions in the lattice Boltzmann method.

Zhaoli Guo, Xuhui Meng, Zhaoli Guo, 2016, Physical review. E.

Preconditioned multiple-relaxation-time lattice Boltzmann equation model for incompressible flow in porous media

Zhaoli Guo, Liang Wang, Xuhui Meng, 2018, Physical Review E.

Pore-scale study on reactive mixing of miscible solutions with viscous fingering in porous media

Zhaoli Guo, Xuhui Meng, T. Lei, 2017 .

Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems

G. Karniadakis, K. Linka, E. Kuhl, 2022, ArXiv.

Uncertainty quantification on the macroscopic properties of heterogeneous porous media

D. Xiu, Peng Wang, Xuhui Meng, 2018, Physical Review E.

Physics-informed neural networks for solving forward and inverse flow problems via the Boltzmann-BGK formulation

George Em Karniadakis, Xuhui Meng, Qin Lou, 2020, J. Comput. Phys..

A multiscale study of density-driven flow with dissolution in porous media

Zhaoli Guo, Xuhui Meng, Haoran Sun, 2020 .

Discrete effect on single-node boundary schemes of lattice Bhatnagar–Gross–Krook model for convection-diffusion equations

Liang Wang, Tian-Hu Wang, Xuhui Meng, 2019, International Journal of Modern Physics C.

Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems

G. Karniadakis, K. Linka, E. Kuhl, 2022, Computer Methods in Applied Mechanics and Engineering.

A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data

G. Karniadakis, Zhongqiang Zhang, S. Goswami, 2021, Computer Methods in Applied Mechanics and Engineering.

Multi-fidelity Bayesian Neural Networks: Algorithms and Applications pdf

George Em Karniadakis, Hessam Babaee, Xuhui Meng, 2020, J. Comput. Phys..

B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data

Liu Yang, George Em Karniadakis, Xuhui Meng, 2020, J. Comput. Phys..

A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse PDE problems

George Em Karniadakis, Xuhui Meng, G. Karniadakis, 2019, J. Comput. Phys..

DeepXDE: A Deep Learning Library for Solving Differential Equations pdf

Zhiping Mao, Lu Lu, George E. Karniadakis, 2019, AAAI Spring Symposium: MLPS.

Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems pdf

George Em Karniadakis, Xuhui Meng, Lu Lu, 2021, Computer Methods in Applied Mechanics and Engineering.

Uncertainty Quantification in Scientific Machine Learning: Methods, Metrics, and Comparisons pdf

George Em Karniadakis, Apostolos F. Psaros, Ling Guo, 2022, J. Comput. Phys..

Learning Functional Priors and Posteriors from Data and Physics pdf

George Em Karniadakis, Xuhui Meng, Zhiping Mao, 2021, J. Comput. Phys..

A Localized Mass-Conserving Lattice Boltzmann Approach for Non-Newtonian Fluid Flows

Zhaoli Guo, Xuhui Meng, Liang Wang, 2015 .

Deep neural operator for learning transient response of interpenetrating phase composites subject to dynamic loading

Xuhui Meng, Minglei Lu, Zhaoxu Meng, 2023, Computational Mechanics.

Simulating flow in porous media using the lattice Boltzmann method: Intercomparison of single-node boundary schemes from benchmarking to application

Xiaofan Yang, Liang Wang, Xuhui Meng, 2020 .

Physics-informed neural networks with residual/gradient-based adaptive sampling methods for solving partial differential equations with sharp solutions

Zhiping Mao, Xuhui Meng, 2023, Applied Mathematics and Mechanics.

Physics-informed neural networks for predicting gas flow dynamics and unknown parameters in diesel engines

G. Karniadakis, Xuhui Meng, Kamaljyoti Nath, 2023, ArXiv.

Variational inference in neural functional prior using normalizing flows: application to differential equation and operator learning problems

Xuhui Meng, 2023, Applied Mathematics and Mechanics.

Physics-informed neural networks with residual/gradient-based adaptive sampling methods for solving PDEs with sharp solutions

Zhiping Mao, Xuhui Meng, 2023, ArXiv.

Localized lattice Boltzmann equation model for simulating miscible viscous displacement in porous media

Zhaoli Guo, Xuhui Meng, Zhaoli Guo, 2016 .

NeuralUQ: A comprehensive library for uncertainty quantification in neural differential equations and operators

Apostolos F. Psaros, G. Karniadakis, Xuhui Meng, 2022, ArXiv.

Solution multiplicity and effects of data and eddy viscosity on Navier-Stokes solutions inferred by physics-informed neural networks

G. Karniadakis, Xuhui Meng, Xiao-Qiang Jiang, 2023, 2309.06010.

A Comparative Study on Deep Learning Models for COVID-19 Forecast

Qingyi Lin, Xuhui Meng, Ziyuan Guo, 2023, Healthcare.

B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data

G. Karniadakis, Liu Yang, Xuhui Meng, 2020, J. Comput. Phys..