Xuhui Meng

According to our database1, Xuhui Meng authored at least 17 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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PhD thesis 
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Links

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Bibliography

2024
NeuralUQ: A Comprehensive Library for Uncertainty Quantification in Neural Differential Equations and Operators.
SIAM Rev., February, 2024

Correcting model misspecification in physics-informed neural networks (PINNs).
J. Comput. Phys., 2024

2023
Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons.
J. Comput. Phys., March, 2023

Uncertainty quantification for noisy inputs-outputs in physics-informed neural networks and neural operators.
CoRR, 2023

Physics-informed neural networks for predicting gas flow dynamics and unknown parameters in diesel engines.
CoRR, 2023

Deep neural operator for learning transient response of interpenetrating phase composites subject to dynamic loading.
CoRR, 2023

Variational inference in neural functional prior using normalizing flows: Application to differential equation and operator learning problems.
CoRR, 2023

Physics-informed neural networks with residual/gradient-based adaptive sampling methods for solving PDEs with sharp solutions.
CoRR, 2023

2022
Learning functional priors and posteriors from data and physics.
J. Comput. Phys., 2022

Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems.
CoRR, 2022

2021
DeepXDE: A Deep Learning Library for Solving Differential Equations.
SIAM Rev., 2021

B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data.
J. Comput. Phys., 2021

Multi-fidelity Bayesian neural networks: Algorithms and applications.
J. Comput. Phys., 2021

Physics-informed neural networks for solving forward and inverse flow problems via the Boltzmann-BGK formulation.
J. Comput. Phys., 2021

Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems.
CoRR, 2021

2020
A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse PDE problems.
J. Comput. Phys., 2020

2019
PPINN: Parareal Physics-Informed Neural Network for time-dependent PDEs.
CoRR, 2019


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