华中科技大学数学与统计学院 副教授
联系方式
邮箱：xuhui_meng@hust.edu.cn
教育经历
2008年9月-2012年6月：华中科技大学能源与动力工程学院本科
2012年9月-2017年9月：华中科技大学能源与动力工程学院煤燃烧国家重点实验室硕博连读
研究方向
数据驱动的深度学习建模与应用；不确定性量化；高性能计算
科研项目
[1]基于物理机理深度学习的高效不确定性量化模型研究，国家自然科学基金委，2023年1月-2025年12月
[2]人工智能科学计算共性平台,科技部科技创新2030-“新一代人工智能”重大项目,2023年3月-2026年2月
[3]内嵌物理机理的贝叶斯深度学习模型发展及其应用，百度公司，2022年9月1号-2023年8月31号
代表性成果
[1] A. F. Psaros#, X. Meng#, Z. Zou, L. Guo, G. E. Karniadakis, Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons, Journal of Computational Physics, 477(2023), 111902.
[2]. Xuhui Meng, Liu Yang, George Em Karniadakis, Learning functional priors and posteriors from data and physics, Journal of Computational Physics, 457 (2022), 111073.
[3]. Lu Lu, Xuhui Meng, Shengze Cai, Zhiping Mao, Somdatta Goswami, Zhongqiang Zhang, George Em Karniadakis, A comprehensive and fair comparison of two neural operators (with practical extensions) based on fair data, 393 (2022), 114778.
[4]. Xuhui Meng, Zhicheng Wang, Dixia Fan, Michael Triantafyllou, George Em Karniadakis, A fast multi-fidelity method with uncertainty quantification for complex data correlations: Application to vortex-induced vibrations of marine risers, Computer Methods in Applied Mechanics and Engineering, 386 (2021) 114212.
[5]. Qin Lou, Xuhui Meng, George Em Karniadakis, Physics-informed neural networks for solving forward and inverse flow problems via the Boltzmann-BGK formulation, Journal of Computational Physics, 447 (2021) 110676.
[6]. Xuhui Meng, Hessam Babaee, George Em Karniadakis, Multi-fidelity Bayesian Neural Networks: Algorithms andApplications, Journal of Computational Physics, 438 (2021) 110361.
[7]. Lu Lu, Xuhui Meng, Zhiping Mao, George Em Karniadakis, DeepXDE: A deep learning library for solving differential equations, SIAM Review, 3 (2021), 208-228.
[8]. Liu Yang, Xuhui Meng, George Em Karniadakis, B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data, Journal of Computational Physics, 25 (2021), 109913.
[9]. Xuhui Meng, George Em Karniadakis, A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse PDE problems, Journal of Computational Physics, 01 (2020), 109020.
[10]. Xuhui Meng, Zhen Li, Donkun Zhang, George Em Karniadakis, PPINN: Parareal physics-informed neural network for time-dependent PDEs, Computer Methods in Applied Mechanics and Engineering, 370 (2020) 113250.
[11]Xuhui Meng, Liang Wang, Weifeng Zhao, Xiaofan Yang, Simulating flow in porous media using the lattice Boltzmann method: Intercomparison of single-node boundary schemes from benchmarking to application, Advances in Water Resources, 141 (2020), 103583.
