Han Gao

Statistical and Nonlinear Physics · with Wang · 2019–2021

A researcher in the Science corpus: 3 papers; trusts 58 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Statistical and Nonlinear Physics — 92 citations into it; #2,522 of 10,127 by trust here, #2,503 by citations
• Artificial Intelligence — 45 citations into it
• Computational Mechanics — 36 citations into it; #2,863 of 10,001 by trust here, #2,644 by citations
• Statistics and Probability — 32 citations into it; #7,881 of 12,932 by trust here, #4,371 by citations
• AI for Physical Sciences — 22 citations into it
• Computer Vision and Pattern Recognition — 21 citations into it
• Aerospace Engineering — 17 citations into it
• Optimization & Training — 11 citations into it

Whom Han Gao trusts overall

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

1. G. Em Karniadakis
2. Maizar Raissi
3. Ruiyang Zhang
4. Paris Perdikaris
5. Jens Berg
6. Luning Sun
7. Geoffrey E. Hinton
8. Xuhui Meng
9. Diederik P. Kingma
10. Lu Lu

Who trusts Han Gao overall

1. Pao-Hsiung Chiu
2. Shengze Cai
3. Salvatore Cuomo
4. Chengping Rao
5. Jian Cheng Wong
6. Zong-Yi Li
7. Matthew J. Zahr
8. Jian-Xun Wang
9. Yang Liu
10. C. Ooi

Papers from Han Gao's perspective

Ordered by how strongly the researchers Han Gao 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. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. GENERATIVE ADVERSARIAL NETS

Their papers

• Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data (2019) — 1,046 citations
• Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems (2021) — 310 citations
• PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain (2020) — 646 citations