Zhicheng Wang
Statistical and Nonlinear Physics · Acta Mechanica Sinica · 2018–2021
A researcher in the Science corpus: 5 papers; trusts 29 researchers and is trusted by 10 researchers here, as inferred from the citation graph.
Subfields
- Statistical and Nonlinear Physics — 76 citations into it; #2,844 of 10,127 by trust here, #1,677 by citations
- Artificial Intelligence — 55 citations into it
- Computational Mechanics — 42 citations into it; #4,655 of 10,001 by trust here, #2,156 by citations
- AI for Physical Sciences — 29 citations into it
- Statistics and Probability — 20 citations into it
- Aerospace Engineering — 19 citations into it; #6,071 of 6,421 by trust here, #3,764 by citations
- Computer Vision and Pattern Recognition — 17 citations into it
- Optimization & Training — 15 citations into it
Whom Zhicheng Wang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- G. Em Karniadakis
- Maizar Raissi
- Paris Perdikaris
- Diederik P. Kingma
- Ameya Dilip Jagtap
- Markus Raffel
- Jimmy Ba
- Atilim Gunes Baydin
- Lu Lu
- Liu Yang
Who trusts Zhicheng Wang overall
- Ameya Dilip Jagtap
- G. Em Karniadakis
- Shengze Cai
- Salvatore Cuomo
- Majid Rasht-Behesht
- Zhiping Mao
- Jared D. Willard
- Luning Sun
- Xiaowei Jin
- Pao-Hsiung Chiu
Papers from Zhicheng Wang's perspective
Ordered by how strongly the researchers Zhicheng Wang 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)
- GENERATIVE ADVERSARIAL NETS
- 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
- 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
- Physics-informed neural networks with hard constraints for inverse design (2021) — 806 citations
- Deep learning of vortex-induced vibrations (2018) — 440 citations