Shaowu Pan

Statistical and Nonlinear Physics · 2019–2020

A researcher in the Science corpus: 3 papers; trusts 19 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Statistical and Nonlinear Physics — 49 citations into it; #3,056 of 10,127 by trust here, #2,263 by citations
• Artificial Intelligence — 33 citations into it
• Computer Vision and Pattern Recognition — 22 citations into it
• Statistics and Probability — 21 citations into it; #7,238 of 12,932 by trust here, #4,371 by citations
• Computational Mechanics — 18 citations into it; #3,364 of 10,001 by trust here, #2,509 by citations
• AI for Physical Sciences — 14 citations into it
• Optimization & Training — 9 citations into it
• Atmospheric Science — 8 citations into it

Whom Shaowu Pan trusts overall

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

1. Maizar Raissi
2. G. Em Karniadakis
3. Yoshua Bengio
4. Xavier Glorot
5. Yann LeCun
6. Geoffrey E. Hinton
7. Andrew L. Maas
8. Diederik P. Kingma
9. Paris Perdikaris
10. Alex Krizhevsky

Who trusts Shaowu Pan overall

1. Han Gao
2. Zijie Li
3. Shuhao Cao
4. Chengping Rao
5. Luning Sun
6. Jian-Xun Wang
7. Sifan Wang

Papers from Shaowu Pan's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Going deeper with convolutions

Their papers

• Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data (2019) — 1,046 citations
• Stiff-PINN: Physics-Informed Neural Network for Stiff Chemical Kinetics (2020) — 315 citations
• Prediction of aerodynamic flow fields using convolutional neural networks (2019) — 631 citations