E Weinan
Impact in
- Statistical and Nonlinear Physics top 0.05%
- Model Reduction and Neural Networks
- Computational Mechanics top 0.05%
- Advanced Numerical Methods in Computational Mathematics
- Fluid Dynamics and Turbulent Flows
Papers in
-
- Advanced Numerical Methods in Computational Mathematics 46
- Fluid Dynamics and Turbulent Flows 38
-
- Model Reduction and Neural Networks 23
- Co-authors
- Eric Vanden‐Eijnden (27 shared papers)Jiequn Han (14 shared papers)Weiqing Ren (19 shared papers)Linfeng Zhang (24 shared papers)Bjorn E. Engquist (9 shared papers)Arnulf Jentzen (9 shared papers)Han Dong Wang (15 shared papers)Roberto Car (13 shared papers)
- Journals
- Journal of Computational Physics (22 papers)Communications in Mathematical Sciences (14 papers)Communications on Pure and Applied Mathematics (13 papers)The Journal of Chemical Physics (12 papers)Physical Review B (10 papers)
- Partner nations
- United StatesChinaSwitzerland
In The Last Decade
E Weinan
293 papers receiving 24.4k citations
E Weinan's Hit Papers
Peers
Comparison fields: 5 of 182
- Statistical and Nonlinear Physics 4.8k
- Computational Mechanics 6.5k
- Computational Theory and Mathematics 4.4k
- Numerical Analysis 985
- Applied Mathematics 1.9k
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-authors
The 25 scholars most cited alongside E Weinan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 302 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics Hit paper breakdown → | 2018 | 1688 |
| 2 | DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics Hit paper breakdown → | 2018 | 1605 |
| 3 | Solving high-dimensional partial differential equations using deep learning Hit paper breakdown → | 2018 | 1138 |
| 4 | String method for the study of rare events Hit paper breakdown → | 2002 | 1069 |
| 5 | The Deep Ritz Method: A Deep Learning-Based Numerical Algorithm for Solving Variational Problems Hit paper breakdown → | 2018 | 985 |
| 6 | The Heterognous Multiscale Methods Hit paper breakdown → | 2003 | 784 |
| 7 | DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models Hit paper breakdown → | 2020 | 707 |
| 8 | Simplified and improved string method for computing the minimum energy paths in barrier-crossing events Hit paper breakdown → | 2007 | 561 |
| 9 | Heterogeneous Multiscale Methods: A Review Hit paper breakdown → | 2007 | 535 |
| 10 | Deep Learning-Based Numerical Methods for High-Dimensional Parabolic Partial Differential Equations and Backward Stochastic Differential Equations Hit paper breakdown → | 2017 | 522 |
| 11 | Transition-Path Theory and Path-Finding Algorithms for the Study of Rare Events Hit paper breakdown → | 2008 | 490 |
| 12 | 2005 | 485 | |
| 13 | DeePMD-kit v2: A software package for deep potential models Hit paper breakdown → | 2023 | 417 |
| 14 | A Proposal on Machine Learning via Dynamical Systems Hit paper breakdown → | 2017 | 387 |
| 15 | The heterogeneous multiscale method Hit paper breakdown → | 2012 | 386 |
| 16 | 2006 | 370 | |
| 17 | Onsager's conjecture on the energy conservation for solutions of Euler's equation Hit paper breakdown → | 1994 | 364 |
| 18 | 1996 | 325 | |
| 19 | Principles of Multiscale Modeling | 2011 | 298 |
| 20 | Phase Diagram of a Deep Potential Water Model Hit paper breakdown → | 2021 | 276 |
About E Weinan
E Weinan is a scholar working on Computational Mechanics, Statistical and Nonlinear Physics, Computational Theory and Mathematics, Condensed Matter Physics and Atomic and Molecular Physics, and Optics, having authored 302 papers that have together received 25.6k indexed citations. Recurring topics across this work include Advanced Mathematical Modeling in Engineering (49 papers), Advanced Numerical Methods in Computational Mathematics (46 papers), Fluid Dynamics and Turbulent Flows (38 papers), Machine Learning in Materials Science (33 papers), Theoretical and Computational Physics (32 papers), Composite Material Mechanics (24 papers), Advanced Chemical Physics Studies (23 papers) and Model Reduction and Neural Networks (23 papers). The work is most often cited by research in Statistical and Nonlinear Physics (4.8k citations), Computational Mechanics (6.5k citations), Computational Theory and Mathematics (4.4k citations), Numerical Analysis (985 citations) and Applied Mathematics (1.9k citations). E Weinan has collaborated with scholars based in United States, China and Switzerland. Frequent co-authors include Eric Vanden‐Eijnden, Jiequn Han, Weiqing Ren, Linfeng Zhang, Bjorn E. Engquist, Arnulf Jentzen, Han Dong Wang, Roberto Car, Bing Yu and Han Wang. Their work appears in journals such as Journal of Computational Physics, Communications in Mathematical Sciences, Communications on Pure and Applied Mathematics, The Journal of Chemical Physics and Physical Review B.
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.