Shengze Cai
Statistical and Nonlinear Physics · 2020–2021
A researcher in the Science corpus: 5 papers; trusts 34 researchers and is trusted by 15 researchers here, as inferred from the citation graph.
Subfields
- Statistical and Nonlinear Physics — 82 citations into it; #2,641 of 10,127 by trust here, #1,949 by citations
- Computational Mechanics — 45 citations into it; #3,152 of 10,001 by trust here, #2,070 by citations
- Artificial Intelligence — 39 citations into it
- AI for Physical Sciences — 23 citations into it
- Aerospace Engineering — 19 citations into it; #3,473 of 6,421 by trust here, #2,245 by citations
- Statistics and Probability — 18 citations into it
- Computer Vision and Pattern Recognition — 14 citations into it
- Optimization & Training — 12 citations into it
Whom Shengze Cai trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- G. Em Karniadakis
- Maizar Raissi
- Ameya Dilip Jagtap
- Diederik P. Kingma
- Paris Perdikaris
- Markus Raffel
- Sifan Wang
- Jimmy Ba
- Lu Lu
- Liu Yang
Who trusts Shengze Cai overall
- Salvatore Cuomo
- Majid Rasht-Behesht
- G. Em Karniadakis
- Vincenzo Schiano Di Cola
- C. Huber
- Zhiping Mao
- Khemraj Shukla
- Zhicheng Wang
- Chengping Rao
- Han Gao
Papers from Shengze Cai's perspective
Ordered by how strongly the researchers Shengze Cai 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
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- ImageNet: A large-scale hierarchical image database
- Going deeper with convolutions
Their papers
- Physics-Informed Neural Networks for Heat Transfer Problems (2021) — 1,255 citations
- Physics-informed neural networks (PINNs) for fluid mechanics: a review (2021) — 2,043 citations
- A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data (2021) — 711 citations
- Flow over an espresso cup: inferring 3-D velocity and pressure fields from tomographic background oriented Schlieren via physics-informed neural networks (2021) — 344 citations
- NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations (2020) — 1,247 citations