Xuhui Meng

Statistical and Nonlinear Physics · 2016–2022

A researcher in the Science corpus: 8 papers; trusts 66 researchers and is trusted by 40 researchers here, as inferred from the citation graph.

Subfields

• Statistical and Nonlinear Physics — 104 citations into it; #671 of 10,127 by trust here, #918 by citations
• Artificial Intelligence — 83 citations into it; #11,609 of 35,345 by trust here, #15,236 by citations
• Computational Mechanics — 47 citations into it; #4,839 of 10,001 by trust here, #6,287 by citations
• Statistics and Probability — 40 citations into it; #3,541 of 12,932 by trust here, #2,309 by citations
• AI for Physical Sciences — 24 citations into it
• Computer Vision and Pattern Recognition — 21 citations into it
• Optimization & Training — 18 citations into it
• Probabilistic & Bayesian Methods — 18 citations into it

Whom Xuhui Meng trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Maizar Raissi
2. G. Em Karniadakis
3. Paris Perdikaris
4. Lu Lu
5. Radford M. Neal
6. Diederik P. Kingma
7. Floris Takens
8. Michael S. Eldred
9. Dongkun Zhang
10. Geoffrey E. Hinton

Who trusts Xuhui Meng overall

1. Lu Lu
2. G. Em Karniadakis
3. Han Gao
4. Shengze Cai
5. Salvatore Cuomo
6. Luning Sun
7. Jared D. Willard
8. Pengzhan Jin
9. Weiqi Ji
10. M. Giselle Fernández-Godino

Papers from Xuhui Meng's perspective

Ordered by how strongly the researchers Xuhui Meng trusts endorse each paper. Same corpus, a different order for every researcher.

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. ImageNet classification with deep convolutional neural networks
4. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Learning Multiple Layers of Features from Tiny Images
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. ImageNet: A large-scale hierarchical image database
10. Going deeper with convolutions

Their papers

• DeepXDE: A Deep Learning Library for Solving Differential Equations (2019) — 2,276 citations
• A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse PDE problems (2019) — 673 citations
• Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons (2022) — 456 citations
• Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems (2021) — 771 citations
• A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data (2021) — 711 citations
• B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data (2020) — 1,163 citations
• PPINN: Parareal Physics-Informed Neural Network for time-dependent PDEs (2019) — 601 citations
• Localized lattice Boltzmann equation model for simulating miscible viscous displacement in porous media (2016) — 33 citations