Zhiping Mao
Statistical and Nonlinear Physics · 2016–2022
A researcher in the Science corpus: 8 papers; trusts 45 researchers and is trusted by 33 researchers here, as inferred from the citation graph.
Subfields
- Statistical and Nonlinear Physics — 96 citations into it; #974 of 10,127 by trust here, #1,256 by citations
- Artificial Intelligence — 52 citations into it
- Computational Mechanics — 42 citations into it; #2,989 of 10,001 by trust here, #2,644 by citations
- Applied Mathematics — 33 citations into it
- Computational Theory and Mathematics — 27 citations into it
- Modeling and Simulation — 27 citations into it
- AI for Physical Sciences — 24 citations into it
- Statistics and Probability — 22 citations into it; #8,247 of 12,932 by trust here, #3,411 by citations
Whom Zhiping Mao trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- G. Em Karniadakis
- Ameya Dilip Jagtap
- Maizar Raissi
- Paris Perdikaris
- Lu Lu
- Diederik P. Kingma
- Guofei Pang
- E. Kharazmi
- Sifan Wang
- Yoshua Bengio
Who trusts Zhiping Mao overall
- G. Em Karniadakis
- Han Gao
- E. Kharazmi
- Siddhartha Mishra
- Salvatore Cuomo
- Ameya Dilip Jagtap
- Lu Lu
- Pao-Hsiung Chiu
- Ji-chao Li
- Shengze Cai
Papers from Zhiping Mao's perspective
Ordered by how strongly the researchers Zhiping Mao 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)
- Going deeper with convolutions
- ImageNet: A large-scale hierarchical image database
Their papers
- DeepXDE: A Deep Learning Library for Solving Differential Equations (2019) — 2,276 citations
- What is the fractional Laplacian? A comparative review with new results (2018) — 476 citations
- Physics-informed neural networks for inverse problems in supersonic flows (2022) — 337 citations
- Physics-informed neural networks (PINNs) for fluid mechanics: a review (2021) — 2,043 citations
- A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data (2021) — 711 citations
- Physics-informed neural networks for high-speed flows (2020) — 1,237 citations
- Well-posedness of the Cahn–Hilliard equation with fractional free energy and its Fourier Galerkin approximation (2017) — 51 citations
- Efficient and accurate spectral method using generalized Jacobi functions for solving Riesz fractional differential equations (2016) — 105 citations