E. Kharazmi

Statistical and Nonlinear Physics · 2019–2020

A researcher in the Science corpus: 3 papers; trusts 23 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Statistical and Nonlinear Physics — 31 citations into it; #1,760 of 10,127 by trust here, #3,735 by citations
• Artificial Intelligence — 22 citations into it; #22,309 of 35,345 by trust here, #25,809 by citations
• Computational Mechanics — 15 citations into it; #3,646 of 10,001 by trust here, #5,813 by citations
• AI for Physical Sciences — 13 citations into it
• Optimization & Training — 7 citations into it
• Numerical Methods & Scientific Computing — 6 citations into it
• Aerospace Engineering — 5 citations into it; #3,478 of 6,421 by trust here, #4,388 by citations
• Atmospheric Science — 5 citations into it

Whom E. Kharazmi trusts overall

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

1. G. Em Karniadakis
2. Maizar Raissi
3. E.J. Candes
4. Ingrid Daubechies
5. Diederik P. Kingma
6. Jan S. Hesthaven
7. Paris Perdikaris
8. Xavier Glorot
9. Ameya Dilip Jagtap
10. Zhiping Mao

Who trusts E. Kharazmi overall

1. Ameya Dilip Jagtap
2. G. Em Karniadakis
3. Chengping Rao
4. Khemraj Shukla
5. Salvatore Cuomo
6. Han Gao
7. Shengze Cai
8. Zhiping Mao
9. Sifan Wang
10. Yang Liu

Papers from E. Kharazmi's perspective

Ordered by how strongly the researchers E. Kharazmi 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. Learning Multiple Layers of Features from Tiny Images
7. Attention is All you Need (Neural Information Processing Systems)
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. ImageNet: A large-scale hierarchical image database
10. Going deeper with convolutions

Their papers

• hp-VPINNs: Variational Physics-Informed Neural Networks With Domain Decomposition (2020) — 816 citations
• Conservative physics-informed neural networks on discrete domains for conservation laws: Applications to forward and inverse problems (2020)
• Variational Physics-Informed Neural Networks For Solving Partial Differential Equations (2019) — 339 citations