Jian-Xun Wang

Statistical and Nonlinear Physics · 2015–2021

A researcher in the Science corpus: 7 papers; trusts 98 researchers and is trusted by 30 researchers here, as inferred from the citation graph.

Subfields

• Statistical and Nonlinear Physics — 108 citations into it; #907 of 10,127 by trust here, #1,183 by citations
• Artificial Intelligence — 65 citations into it
• Statistics and Probability — 61 citations into it; #6,721 of 12,932 by trust here, #3,411 by citations
• Computational Mechanics — 60 citations into it; #1,216 of 10,001 by trust here, #1,272 by citations
• Computer Vision and Pattern Recognition — 27 citations into it; #15,082 of 21,575 by trust here, #18,782 by citations
• AI for Physical Sciences — 25 citations into it
• Aerospace Engineering — 22 citations into it
• Environmental Engineering — 21 citations into it

Whom Jian-Xun Wang trusts overall

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

1. Maizar Raissi
2. G. Em Karniadakis
3. Jeremy A. Templeton
4. Trevor J. Hastie
5. Eric R. Ziegel
6. Diederik P. Kingma
7. Tin Kam Ho
8. Naomi S. Altman
9. Stephen B. Pope
10. Niko Speybroeck

Who trusts Jian-Xun Wang overall

1. Anand Pratap Singh
2. Heng Xiao
3. Karthik Duraisamy
4. Han Gao
5. Navid Zobeiry
6. Ricardo Vinuesa
7. Shivaji Medida
8. Shengze Cai
9. Xiaowei Jin
10. Anuj Karpatne

Papers from Jian-Xun Wang's perspective

Ordered by how strongly the researchers Jian-Xun Wang 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. ImageNet: A large-scale hierarchical image database
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Going deeper with convolutions

Their papers

• Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data (2019) — 1,046 citations
• Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier-Stokes simulations: A data-driven, physics-informed Bayesian approach (2015) — 315 citations
• Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems (2021) — 310 citations
• Physics-constrained bayesian neural network for fluid flow reconstruction with sparse and noisy data (2020) — 212 citations
• PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain (2020) — 646 citations
• Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data (2016) — 668 citations
• Physics-Informed Machine Learning for Predictive Turbulence Modeling: Using Data to Improve RANS Modeled Reynolds Stresses (2016) — 30 citations