Beibei Fu

611 citations
14 papers · 518 · 1 hit paper · h-index 8

Impact in

Papers in

Beibei Fu

12 papers receiving 509 citations

Beibei Fu's Hit Papers

A gradient-enhanced physics-informed neural networks method for the wave equation 2024 · 82 citations
820+1Years since publication255075

Peers

Beibei Fu
Comparison fields: 5 of 61
  • Polymers and Plastics 133
  • Electrical and Electronic Engineering 342
  • Materials Chemistry 196
  • Modeling and Simulation 13
  • Bioengineering 16
Replace Vincent Bayot with:
Vincent Bayot Belgium
Yuchen Yue China
Weiheng Zhong China
Yuta Shiratori Japan
E. P. Kitsyuk Russia
Laurie E. Calvet France
Jingwen Ma China
Csilla Mikó Switzerland
Junchen Zhou China
Beibei Fu relative to Vincent Bayot Belgium Vincent Bayot's profile →
Citations per field
00.5×20×40×60×78×
Vincent Bayot · 1×
Citations per year

Countries citing papers authored by Beibei Fu

Since Specialization
Citations

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

Fields of papers citing papers by Beibei Fu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Beibei Fu, 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 Beibei Fu Line = papers co-authored together Beibei Fu links everyone, so they are left out of the graph.

All Works

14 of 14 papers shown
#Work
1 2018177
2
A gradient-enhanced physics-informed neural networks method for the wave equation
Hit paper breakdown →
202482
3 202061
4 201959
5 202256
6 201747
7 201711
8 202211
9 20186
10 20223
11 20192
12 20202
13 20121
14 20250

About Beibei Fu

Beibei Fu is a scholar working on Electrical and Electronic Engineering, Materials Chemistry, Polymers and Plastics, Biomedical Engineering and Organic Chemistry, having authored 14 papers that have together received 518 indexed citations. Recurring topics across this work include Organic Electronics and Photovoltaics (6 papers), Conducting polymers and applications (4 papers), Advanced Sensor and Energy Harvesting Materials (4 papers), Luminescence and Fluorescent Materials (2 papers), Perovskite Materials and Applications (2 papers), Gas Sensing Nanomaterials and Sensors (2 papers), ZnO doping and properties (1 paper) and Visual perception and processing mechanisms (1 paper). The work is most often cited by research in Polymers and Plastics (133 citations), Electrical and Electronic Engineering (342 citations), Materials Chemistry (196 citations), Modeling and Simulation (13 citations) and Bioengineering (16 citations). Beibei Fu has collaborated with scholars based in China and Singapore. Frequent co-authors include Xiaotao Zhang, Wenping Hu, Rongjin Li, Cong Wang, Huanli Dong, Shengbin Lei, Hongxiang Li, Yonggang Zhen, Fangxu Yang and Xiaochen Ren. Their work appears in journals such as Advanced Materials, Science China Materials, Chemical Communications, Engineering Analysis with Boundary Elements and Advanced Electronic Materials.

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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