Luning Sun

Statistical and Nonlinear Physics · 2019–2020

A researcher in the Science corpus: 3 papers; trusts 63 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Statistical and Nonlinear Physics — 77 citations into it; #2,052 of 10,127 by trust here, #2,158 by citations
• Artificial Intelligence — 42 citations into it
• Statistics and Probability — 34 citations into it; #7,670 of 12,932 by trust here, #4,371 by citations
• Computational Mechanics — 30 citations into it; #2,369 of 10,001 by trust here, #2,309 by citations
• Computer Vision and Pattern Recognition — 21 citations into it; #15,054 of 21,575 by trust here, #18,782 by citations
• AI for Physical Sciences — 18 citations into it
• Aerospace Engineering — 12 citations into it
• Optimization & Training — 10 citations into it

Whom Luning Sun trusts overall

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

1. Maizar Raissi
2. G. Em Karniadakis
3. Diederik P. Kingma
4. Paris Perdikaris
5. Yoshua Bengio
6. Prajit Ramachandran
7. Geoffrey E. Hinton
8. Charles Blundell
9. Jimmy Ba
10. Yann LeCun

Who trusts Luning Sun overall

1. Han Gao
2. Chengping Rao
3. Shengze Cai
4. Sifan Wang
5. Zixue Xiang
6. Jian-Xun Wang
7. Yang Liu
8. Salvatore Cuomo
9. Zijie Li
10. Matthew J. Zahr

Papers from Luning Sun's perspective

Ordered by how strongly the researchers Luning Sun 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. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Going deeper with convolutions
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data (2019) — 1,046 citations
• Physics-constrained bayesian neural network for fluid flow reconstruction with sparse and noisy data (2020) — 212 citations
• PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain (2020) — 646 citations