Jian‐Guo Liu

Computational Mechanics · 2004–2018

A researcher in the Science corpus: 11 papers; trusts 29 researchers and is trusted by 46 researchers here, as inferred from the citation graph.

Subfields

• Computational Mechanics — 26 citations into it; #2,830 of 10,001 by trust here, #5,157 by citations
• Optimization & Training — 17 citations into it
• Statistical and Nonlinear Physics — 13 citations into it; #867 of 10,127 by trust here, #3,513 by citations
• Modeling and Simulation — 11 citations into it; #324 of 2,105 by trust here, #411 by citations
• Applied Mathematics — 10 citations into it; #3,842 of 7,620 by trust here, #3,100 by citations
• Numerical Analysis — 9 citations into it; #1,373 of 3,586 by trust here, #2,430 by citations
• Molecular Biology — 8 citations into it; #733 of 17,773 by trust here, #6,746 by citations
• Computational Theory and Mathematics — 7 citations into it; #11,282 of 12,182 by trust here, #9,804 by citations

Whom Jian‐Guo Liu trusts overall

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

1. Jean-Luc Guermond
2. Vincent Calvez
3. José Antonio Carrillo
4. Felipe Cucker
5. Pierre Degond
6. W. E
7. Jie Shen
8. Herbert Amann
9. Hans Johnston
10. Alexander Lorz

Who trusts Jian‐Guo Liu overall

1. Michael Winkler
2. Youshan Tao
3. Qingshan Zhang
4. Seung‐Yeal Ha
5. Young-Pil Choi
6. Sébastien Motsch
7. Felipe Cucker
8. Yulan Wang
9. Dongho Chae
10. Johannes Lankeit

Papers from Jian‐Guo Liu's perspective

Ordered by how strongly the researchers Jian‐Guo Liu trusts endorse each paper. Same corpus, a different order for every researcher.

1. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
2. On the importance of initialization and momentum in deep learning
3. Large Scale Distributed Deep Networks
4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
5. Emergent Behavior in Flocks
6. Volume effects in the Keller-Segel model : energy estimates preventing blow-up
7. ImageNet classification with deep convolutional neural networks
8. Swarming Patterns in a Two-Dimensional Kinematic Model for Biological Groups (SIAM Journal on Applied Mathematics)
9. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
10. Gradient-based learning applied to document recognition

Their papers

• On the diffusion approximation of nonconvex stochastic gradient descent (2017) — 157 citations
• A coupled chemotaxis-fluid model: Global existence (2011) — 319 citations
• A simple proof of the Cucker-Smale flocking dynamics and mean-field limit (2009) — 600 citations
• A Note on Aubin-Lions-Dubinskiĭ Lemmas (2013) — 98 citations
• Rational solutions and lump solutions to a non-isospectral and generalized variable-coefficient Kadomtsev–Petviashvili equation (2018) — 113 citations
• Uniform spectral convergence of the stochastic Galerkin method for the linear transport equations with random inputs in diffusive regime and a micro–macro decomposition-based asymptotic-preserving method (2017) — 49 citations
• An All-Speed Asymptotic-Preserving Method for the Isentropic Euler and Navier-Stokes Equations (2012) — 153 citations
• Well-posedness for Hall-magnetohydrodynamics (2012) — 281 citations
• Kinetic formulation and global existence for the Hall-Magneto-hydrodynamics system (2011) — 264 citations
• Stability and convergence of efficient Navier‐Stokes solvers via a commutator estimate (2007) — 81 citations
• Accurate, stable and efficient Navier-Stokes solvers based on explicit treatment of the pressure term (2004) — 231 citations