Zhongqiang Zhang
Statistical and Nonlinear Physics · 2018–2021
A researcher in the Science corpus: 5 papers; trusts 25 researchers and is trusted by 26 researchers here, as inferred from the citation graph.
Subfields
- Statistical and Nonlinear Physics — 60 citations into it; #1,169 of 10,127 by trust here, #1,725 by citations
- Artificial Intelligence — 44 citations into it; #17,729 of 35,345 by trust here, #15,004 by citations
- Computational Mechanics — 21 citations into it; #3,645 of 10,001 by trust here, #4,450 by citations
- Model Architectures — 18 citations into it
- AI for Physical Sciences — 17 citations into it
- Statistics and Probability — 16 citations into it
- Computer Vision and Pattern Recognition — 13 citations into it
- Numerical Methods & Scientific Computing — 9 citations into it
Whom Zhongqiang Zhang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- G. Em Karniadakis
- Maizar Raissi
- E.J. Candes
- Ingrid Daubechies
- Diederik P. Kingma
- Paris Perdikaris
- Lu Lu
- Xavier Glorot
- Jan S. Hesthaven
- Yoshua Bengio
Who trusts Zhongqiang Zhang overall
- Ameya Dilip Jagtap
- Salvatore Cuomo
- G. Em Karniadakis
- Sifan Wang
- Han Gao
- Chengping Rao
- Khemraj Shukla
- E. Kharazmi
- Zhiping Mao
- Shengze Cai
Papers from Zhongqiang Zhang's perspective
Ordered by how strongly the researchers Zhongqiang Zhang trusts endorse each paper. Same corpus, a different order for every researcher.
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Learning Multiple Layers of Features from Tiny Images
- Attention is All you Need (Neural Information Processing Systems)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Understanding the difficulty of training deep feedforward neural networks
- Going deeper with convolutions
Their papers
- A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data (2021) — 711 citations
- hp-VPINNs: Variational Physics-Informed Neural Networks With Domain Decomposition (2020) — 816 citations
- Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators (2019) — 4,061 citations
- Variational Physics-Informed Neural Networks For Solving Partial Differential Equations (2019) — 339 citations
- Numerical Methods for Stochastic Partial Differential Equations with White Noise (2018) — 124 citations