Sifan Wang
Statistical and Nonlinear Physics · 2020–2021
A researcher in the Science corpus: 6 papers; trusts 51 researchers and is trusted by 39 researchers here, as inferred from the citation graph.
Subfields
- Statistical and Nonlinear Physics — 106 citations into it; #687 of 10,127 by trust here, #1,100 by citations
- Artificial Intelligence — 79 citations into it
- Computational Mechanics — 47 citations into it; #2,736 of 10,001 by trust here, #2,942 by citations
- Computer Vision and Pattern Recognition — 39 citations into it; #17,242 of 21,575 by trust here, #18,266 by citations
- AI for Physical Sciences — 38 citations into it; #90 of 247 by trust here, #184 by citations
- Statistics and Probability — 27 citations into it; #11,260 of 12,932 by trust here, #5,126 by citations
- Optimization & Training — 23 citations into it; #1,422 of 2,601 by trust here, #1,571 by citations
- Model Architectures — 18 citations into it
Whom Sifan Wang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Maizar Raissi
- Paris Perdikaris
- Xavier Glorot
- Yoshua Bengio
- Diederik P. Kingma
- G. Em Karniadakis
- Yibo Yang
- Geoffrey E. Hinton
- Yann LeCun
- Alex Krizhevsky
Who trusts Sifan Wang overall
- Shuhao Cao
- Shengze Cai
- Benjamin Moseley
- Salvatore Cuomo
- G. Em Karniadakis
- Haixu Wu
- Ameya Dilip Jagtap
- Chen-Chun Wu
- Lu Lu
- Levi D. McClenny
Papers from Sifan Wang's perspective
Ordered by how strongly the researchers Sifan Wang trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- When and why PINNs fail to train: A neural tangent kernel perspective (2020) — 1,613 citations
- Physics-Informed Neural Networks for Heat Transfer Problems (2021) — 1,255 citations
- Learning the solution operator of parametric partial differential equations with physics-informed DeepONets (2021) — 1,131 citations
- Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks (2021) — 1,561 citations
- On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks (2020) — 779 citations
- Understanding and mitigating gradient pathologies in physics-informed neural networks (2020) — 361 citations