E Weinan
About
In The Last Decade
E Weinan
285 papers receiving 21.2k citations
Hit Papers
Peers
Comparison fields: 5 of 180
- Materials Chemistry 6.7k
- Computational Mechanics 5.8k
- Statistical and Nonlinear Physics 4.3k
- Computational Theory and Mathematics 3.8k
- Atomic and Molecular Physics, and Optics 3.3k
Countries citing papers authored by E Weinan
This map shows the geographic impact of E Weinan's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by E Weinan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites E Weinan more than expected).
Fields of papers citing papers by E Weinan
This network shows the impact of papers produced by E Weinan. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by E Weinan. The network helps show where E Weinan may publish in the future.
Co-authorship network of co-authors of E Weinan
This figure shows the co-authorship network connecting the top 25 collaborators of E Weinan. A scholar is included among the top collaborators of E Weinan based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with E Weinan. E Weinan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 2 | |
| 4 | DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models breakdown → | 586 |
| 5 | Barron Spaces and the Compositional Function Spaces for Neural Network Models. | 27 |
| 6 | 56 | |
| 7 | A Mean-Field Optimal Control Formulation of Deep Learning | 1 |
| 8 | How SGD Selects the Global Minima in Over-parameterized Learning: A Dynamical Stability Perspective | 26 |
| 9 | A Priori Estimates for Two-layer Neural Networks | 3 |
| 10 | Convolutional neural networks with low-rank regularization | 171 |
| 11 | Linear scaling algorithms for solving high-dimensional nonlinear parabolic differential equations | 3 |
| 12 | The heterogeneous multiscale method: A ten-year review | 1 |
| 13 | Optimized local basis function for Kohn-Sham density functional theory | 3 |
| 14 | Effectiveness of implicit methods for stiff stochastic differential equations | 34 |
| 15 | Heterogeneous multiscale methods: A review breakdown → | 481 |
| 16 | シート,プレートおよびロッドの弾性変形に対する一般化したCauchy-Born則:原子レベル模型から連続体模型の導出 | 9 |
| 17 | The heterogeneous multi-scale method: A mathematical framework for multi-scale modeling | 1 |
| 18 | ANALYSIS OF MULTISCALE METHODS ∗1) | 25 |
| 19 | Multi-scale Modeling and Computation | 114 |
| 20 | 32 |
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.