Xuhui Meng - RankMe

28 papers Journal 27Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Scalable physics-informed deep generative model for solving forward and inverse stochastic differential equations. Neural Networks Shaoqian Zhou, Wen You, Ling Guo, Xuhui Meng
2025	J	`jnl`	A Machine Learning Framework for Climate-Resilient Insurance and Real Estate Decisions. CoRR Lang Qin, Yuejin Xie, Daili Hua, Xuhui Meng
2025	J	`jnl`	Monte Carlo physics-informed neural networks for multiscale heat conduction via phonon Boltzmann transport equation. J. Comput. Phys. Qingyi Lin, Chuang Zhang, Xuhui Meng, Zhaoli Guo
2024	J	`jnl`	Correcting model misspecification in physics-informed neural networks (PINNs). J. Comput. Phys. Zongren Zou, Xuhui Meng, George Em Karniadakis
2024	J	`jnl`	NeuralUQ: A Comprehensive Library for Uncertainty Quantification in Neural Differential Equations and Operators. SIAM Rev. Zongren Zou, Xuhui Meng, Apostolos F. Psaros, George E. Karniadakis
2023	J	`jnl`	Correcting model misspecification in physics-informed neural networks (PINNs). CoRR Zongren Zou, Xuhui Meng, George Em Karniadakis
2023	J	`jnl`	Deep neural operator for learning transient response of interpenetrating phase composites subject to dynamic loading. CoRR Minglei Lu, Ali Mohammadi, Zhaoxu Meng, Xuhui Meng, Gang Li, Zhen Li
2023	J	`jnl`	Physics-informed neural networks for predicting gas flow dynamics and unknown parameters in diesel engines. CoRR Kamaljyoti Nath, Xuhui Meng, Daniel J. Smith, George Em Karniadakis
2023	J	`jnl`	Physics-informed neural networks with residual/gradient-based adaptive sampling methods for solving PDEs with sharp solutions. CoRR Zhiping Mao, Xuhui Meng
2023	J	`jnl`	Uncertainty quantification for noisy inputs-outputs in physics-informed neural networks and neural operators. CoRR Zongren Zou, Xuhui Meng, George Em Karniadakis
2023	J	`jnl`	Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons. J. Comput. Phys. Apostolos F. Psaros, Xuhui Meng, Zongren Zou, Ling Guo, George Em Karniadakis
2023	J	`jnl`	Variational inference in neural functional prior using normalizing flows: Application to differential equation and operator learning problems. CoRR Xuhui Meng
2022	J	`jnl`	Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems. CoRR Kevin Linka, Amelie Schäfer, Xuhui Meng, Zongren Zou, George Em Karniadakis, Ellen Kuhl
2022	J	`jnl`	Learning functional priors and posteriors from data and physics. J. Comput. Phys. Xuhui Meng, Liu Yang, Zhiping Mao, José del Águila Ferrandis, George Em Karniadakis
2022	J	`jnl`	NeuralUQ: A comprehensive library for uncertainty quantification in neural differential equations and operators. CoRR Zongren Zou, Xuhui Meng, Apostolos F. Psaros, George Em Karniadakis
2022	J	`jnl`	Uncertainty Quantification in Scientific Machine Learning: Methods, Metrics, and Comparisons. CoRR Apostolos F. Psaros, Xuhui Meng, Zongren Zou, Ling Guo, George Em Karniadakis
2021	J	`jnl`	B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data. J. Comput. Phys. Liu Yang, Xuhui Meng, George Em Karniadakis
2021	J	`jnl`	DeepXDE: A Deep Learning Library for Solving Differential Equations. SIAM Rev. Lu Lu, Xuhui Meng, Zhiping Mao, George Em Karniadakis
2021	J	`jnl`	Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems. CoRR Jeremy Yu, Lu Lu, Xuhui Meng, George Em Karniadakis
2021	J	`jnl`	Learning Functional Priors and Posteriors from Data and Physics. CoRR Xuhui Meng, Liu Yang, Zhiping Mao, José del Águila Ferrandis, George Em Karniadakis
2021	J	`jnl`	Multi-fidelity Bayesian neural networks: Algorithms and applications. J. Comput. Phys. Xuhui Meng, Hessam Babaee, George Em Karniadakis
2021	J	`jnl`	Physics-informed neural networks for solving forward and inverse flow problems via the Boltzmann-BGK formulation. J. Comput. Phys. Qin Lou, Xuhui Meng, George Em Karniadakis
2020	J	`jnl`	A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse PDE problems. J. Comput. Phys. Xuhui Meng, George Em Karniadakis
2020	J	`jnl`	B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data. CoRR Liu Yang, Xuhui Meng, George Em Karniadakis
2020	—	`conf`	DeepXDE: A Deep Learning Library for Solving Differential Equations. AAAI Spring Symposium: MLPS Lu Lu, Xuhui Meng, Zhiping Mao, George Em Karniadakis
2020	J	`jnl`	Multi-fidelity Bayesian Neural Networks: Algorithms and Applications. CoRR Xuhui Meng, Hessam Babaee, George Em Karniadakis
2019	J	`jnl`	DeepXDE: A deep learning library for solving differential equations. CoRR Lu Lu, Xuhui Meng, Zhiping Mao, George E. Karniadakis
2019	J	`jnl`	PPINN: Parareal Physics-Informed Neural Network for time-dependent PDEs. CoRR Xuhui Meng, Zhen Li, Dongkun Zhang, George Em Karniadakis