Fei Lu

1.6k citations
62 papers · 890 · h-index 17

Impact in

Papers in

Fei Lu

60 papers receiving 863 citations

Peers

Fei Lu
Comparison fields: 5 of 134
  • Statistics and Probability 153
  • Statistical and Nonlinear Physics 194
  • Statistics, Probability and Uncertainty 90
  • Modeling and Simulation 39
  • Nuclear and High Energy Physics 89
Replace Gabriel Stoltz with:
Gabriel Stoltz France
Christopher Rackauckas United States
Fuchang Gao United States
André M. C. Souza Brazil
D. Anderson Sweden
Jean‐François Bercher France
Xiantao Li United States
Mark K. Transtrum United States
Malvin H. Kalos United States
Michael Günther Germany
Fei Lu relative to Gabriel Stoltz France Gabriel Stoltz's profile →
Citations per field
00.5×4.7×
Gabriel Stoltz · 1×
Citations per year

Countries citing papers authored by Fei Lu

Since Specialization
Citations

This map shows the geographic impact of Fei Lu'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 Fei Lu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fei Lu more than expected).

Fields of papers citing papers by Fei Lu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Fei Lu. 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 Fei Lu. The network helps show where Fei Lu may publish in the future.

Co-authors

The 25 scholars most cited alongside Fei Lu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Fei Lu Line = papers co-authored together Fei Lu links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 62 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2006126
2 201956
3 201756
4 201549
5 202047
6 201147
7 201246
8 201641
9 200937
10 201437
11 201135
12 201127
13 201224
14 202121
15
Learning interaction kernels in heterogeneous systems of agents from multiple trajectories
202118
16 201216
17 200016
18 202415
19 202214
20 202014

About Fei Lu

Fei Lu is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics, Atomic and Molecular Physics, and Optics, Statistics and Probability and Mathematical Physics, having authored 62 papers that have together received 890 indexed citations. Recurring topics across this work include Statistical Methods and Inference (6 papers), Photorefractive and Nonlinear Optics (5 papers), Probabilistic and Robust Engineering Design (5 papers), Advanced Statistical Methods and Models (5 papers), Solid State Laser Technologies (4 papers), Gaussian Processes and Bayesian Inference (4 papers), Model Reduction and Neural Networks (4 papers) and Climate variability and models (4 papers). The work is most often cited by research in Statistics and Probability (153 citations), Statistical and Nonlinear Physics (194 citations), Statistics, Probability and Uncertainty (90 citations), Modeling and Simulation (39 citations) and Nuclear and High Energy Physics (89 citations). Fei Lu has collaborated with scholars based in China, United States and Germany. Frequent co-authors include K. Krishnamoorthy, Alexandre J. Chorin, Thomas Mathew, Mauro Maggioni, Sui Tang, Yaozhong Hu, Xuemin Tu, Shengrong Guo, Matthias Morzfeld and Lei Lei. Their work appears in journals such as Journal of Computational Physics, Proceedings of the National Academy of Sciences, IEEE Access, Journal of Physics D Applied Physics and Journal of Statistical Computation and Simulation.

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.

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