Pengzhan Jin

Statistical and Nonlinear Physics · 2019–2022

A researcher in the Science corpus: 2 papers; trusts 22 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Statistical and Nonlinear Physics — 36 citations into it; #8,806 of 10,127 by trust here, #3,735 by citations
• Artificial Intelligence — 28 citations into it; #31,666 of 35,345 by trust here, #18,586 by citations
• Model Architectures — 13 citations into it
• Computer Vision and Pattern Recognition — 11 citations into it
• AI for Physical Sciences — 9 citations into it
• Statistics and Probability — 9 citations into it
• Computational Mechanics — 7 citations into it
• Mathematical Physics — 6 citations into it

Whom Pengzhan Jin trusts overall

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

1. Lu Lu
2. Maizar Raissi
3. G. Em Karniadakis
4. Diederik P. Kingma
5. Alireza Yazdani
6. Paris Perdikaris
7. Guofei Pang
8. Justin A. Sirignano
9. E. Weinan
10. Xuhui Meng

Who trusts Pengzhan Jin overall

1. Nikola B. Kovachki
2. Zhongkai Hao
3. Lu Lu
4. Sifan Wang
5. Somdatta Goswami

Papers from Pengzhan Jin's perspective

Ordered by how strongly the researchers Pengzhan Jin 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Going deeper with convolutions
10. GENERATIVE ADVERSARIAL NETS

Their papers

• MIONet: Learning multiple-input operators via tensor product (2022) — 308 citations
• Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators (2019) — 4,061 citations