Xiaowei Jin

Statistical and Nonlinear Physics · 2018–2020

A researcher in the Science corpus: 2 papers; trusts 13 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Statistical and Nonlinear Physics — 17 citations into it; #7,331 of 10,127 by trust here, #5,011 by citations
• Computational Mechanics — 16 citations into it; #6,324 of 10,001 by trust here, #4,315 by citations
• AI for Physical Sciences — 5 citations into it
• Artificial Intelligence — 5 citations into it

Whom Xiaowei Jin trusts overall

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

1. Maizar Raissi
2. G. Em Karniadakis
3. Diederik P. Kingma
4. Xavier Glorot
5. Paris Perdikaris
6. Ameya Dilip Jagtap
7. Julia Ling
8. Yoshua Bengio
9. Jimmy Ba
10. Sifan Wang

Who trusts Xiaowei Jin overall

1. Shengze Cai
2. Chengping Rao
3. Han Gao
4. Bo Liu
5. Salvatore Cuomo
6. Zhicheng Wang
7. G. Em Karniadakis
8. Sifan Wang
9. Yang Liu
10. Matthew J. Zahr

Papers from Xiaowei Jin's perspective

Ordered by how strongly the researchers Xiaowei Jin 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. Going deeper with convolutions

Their papers

• NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations (2020) — 1,247 citations
• Prediction model of velocity field around circular cylinder over various Reynolds numbers by fusion convolutional neural networks based on pressure on the cylinder (2018) — 309 citations