Jiequn Han
- Materials Chemistry top 2%
- Statistical and Nonlinear Physics top 0.5%
- Electrical and Electronic Engineering top 5%
- Atomic and Molecular Physics, and Optics top 5%
- Computational Mechanics top 2%
- Co-authors
- E WeinanLinfeng ZhangArnulf JentzenHan WangRoberto CarHandong WangBenjamin MollYves Achdou
- Topics
- Model Reduction and Neural Networks (15 papers)Machine Learning in Materials Science (9 papers)Stochastic processes and financial applications (6 papers)
- Partner nations
- United StatesChinaGermany
In The Last Decade
Jiequn Han
40 papers receiving 5.0k citations
Hit Papers
Peers
Comparison fields: 5 of 125
- Materials Chemistry 2.4k
- Statistical and Nonlinear Physics 1.4k
- Electrical and Electronic Engineering 740
- Atomic and Molecular Physics, and Optics 616
- Computational Mechanics 612
Countries citing papers authored by Jiequn Han
This map shows the geographic impact of Jiequn Han'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 Jiequn Han with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jiequn Han more than expected).
Fields of papers citing papers by Jiequn Han
This network shows the impact of papers produced by Jiequn Han. 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 Jiequn Han. The network helps show where Jiequn Han may publish in the future.
Co-authorship network of co-authors of Jiequn Han
This figure shows the co-authorship network connecting the top 25 collaborators of Jiequn Han. A scholar is included among the top collaborators of Jiequn Han 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 Jiequn Han. Jiequn Han is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 5 | |
| 4 | 0 | |
| 5 | 3 | |
| 6 | 7 | |
| 7 | 6 | |
| 8 | 1 | |
| 9 | 9 | |
| 10 | 1 | |
| 11 | 2 | |
| 12 | 3 | |
| 13 | 8 | |
| 14 | 21 | |
| 15 | Perturbed gradient descent with occupation time. | 0 |
| 16 | Deep Fictitious Play for Finding Markovian Nash Equilibrium in Multi-Agent Games | 4 |
| 17 | A Mean-Field Optimal Control Formulation of Deep Learning | 1 |
| 18 | End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems | 129 |
| 19 | DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamicsbreakdown → | 1352 |
| 20 | Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanicsbreakdown → | 1450 |
About Jiequn Han
Jiequn Han is a scholar working on Statistical and Nonlinear Physics, Finance and Management Science and Operations Research, having authored 44 papers that have together received 5.1k indexed citations. Recurring topics across this work include Model Reduction and Neural Networks (15 papers), Machine Learning in Materials Science (9 papers) and Stochastic processes and financial applications (6 papers). The work is most often cited by research in Statistical and Nonlinear Physics (1.4k citations), Materials Chemistry (2.4k citations) and Computational Mathematics (23 citations). Jiequn Han has collaborated with scholars based in United States, China and Germany. Frequent co-authors include E Weinan, Linfeng Zhang, Arnulf Jentzen, Han Wang, Roberto Car, Handong Wang, Benjamin Moll, Yves Achdou, Jianfeng Lu and Pierre-Louis Lions. Their work appears in journals such as Proceedings of the National Academy of Sciences, Physical Review Letters and Physics Today.
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.