Jinlong Fu
- Ocean Engineering top 5%
- Enhanced Oil Recovery Techniques 7
- Mechanics of Materials top 10%
- Hydrocarbon exploration and reservoir analysis 4
- Computational Mechanics top 10%
- Lattice Boltzmann Simulation Studies 4
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- Model Reduction and Neural Networks 3
- Civil and Structural Engineering top 10%
- Structural Health Monitoring Techniques 4
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- Probabilistic and Robust Engineering Design 3
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- Nuclear Engineering Thermal-Hydraulics 2
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- Generative Adversarial Networks and Image Synthesis 2
Jinlong Fu
22 papers receiving 464 citations
Peers
Comparison fields: 5 of 75
- Ocean Engineering 145
- Mechanics of Materials 150
- Computational Mechanics 92
- Statistical and Nonlinear Physics 48
- Civil and Structural Engineering 71
Countries citing papers authored by Jinlong Fu
This map shows the geographic impact of Jinlong 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 Jinlong Fu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jinlong Fu more than expected).
Fields of papers citing papers by Jinlong Fu
This network shows the impact of papers produced by Jinlong 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 Jinlong Fu. The network helps show where Jinlong Fu may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Jinlong Fu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 4 | |
| 2 | 2025 | 1 | |
| 3 | 2025 | 2 | |
| 4 | 2024 | 15 | |
| 5 | 2024 | 36 | |
| 6 | 2024 | 1 | |
| 7 | 2024 | 7 | |
| 8 | 2024 | 1 | |
| 9 | 2024 | 12 | |
| 10 | 2023 | 5 | |
| 11 | 2023 | 8 | |
| 12 | 2023 | 8 | |
| 13 | 2023 | 20 | |
| 14 | 2023 | 8 | |
| 15 | 2023 | 7 | |
| 16 | 2022 | 38 | |
| 17 | 2022 | 44 | |
| 18 | 2021 | 10 | |
| 19 | 2021 | 1 | |
| 20 | 2020 | 38 |
About Jinlong Fu
Jinlong Fu is a scholar working on Ocean Engineering, Statistics, Probability and Uncertainty, Computational Mechanics, Mechanics of Materials and Computer Vision and Pattern Recognition, having authored 22 papers that have together received 477 indexed citations. Recurring topics across this work include Enhanced Oil Recovery Techniques (7 papers), Lattice Boltzmann Simulation Studies (4 papers), Structural Health Monitoring Techniques (4 papers), Hydrocarbon exploration and reservoir analysis (4 papers), Probabilistic and Robust Engineering Design (3 papers), Model Reduction and Neural Networks (3 papers), Nuclear Engineering Thermal-Hydraulics (2 papers) and Generative Adversarial Networks and Image Synthesis (2 papers). The work is most often cited by research in Ocean Engineering (145 citations), Mechanics of Materials (150 citations), Computational Mechanics (92 citations), Statistical and Nonlinear Physics (48 citations) and Civil and Structural Engineering (71 citations). Jinlong Fu has collaborated with scholars based in United Kingdom, China and United States. Frequent co-authors include Chenfeng Li, Hywel Rhys Thomas, Dunhui Xiao, Song Cen, Yong Liu, K.K. Li, Dongfeng Li, Pinghe Ni, I. M. Navon and Min Wang. Their work appears in journals such as Computer Methods in Applied Mechanics and Engineering, Probabilistic Engineering Mechanics, Mechanical Systems and Signal Processing, Computer Physics Communications and Earth-Science Reviews.
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.