Chengfan Gu

3.0k total citations
83 papers, 2.4k citations indexed

About

Chengfan Gu is a scholar working on Biomedical Engineering, Artificial Intelligence and Aerospace Engineering. According to data from OpenAlex, Chengfan Gu has authored 83 papers receiving a total of 2.4k indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Biomedical Engineering, 24 papers in Artificial Intelligence and 24 papers in Aerospace Engineering. Recurrent topics in Chengfan Gu's work include Target Tracking and Data Fusion in Sensor Networks (23 papers), Inertial Sensor and Navigation (22 papers) and Elasticity and Material Modeling (20 papers). Chengfan Gu is often cited by papers focused on Target Tracking and Data Fusion in Sensor Networks (23 papers), Inertial Sensor and Navigation (22 papers) and Elasticity and Material Modeling (20 papers). Chengfan Gu collaborates with scholars based in Australia, China and Hong Kong. Chengfan Gu's co-authors include Yongmin Zhong, László S. Tóth, Bingbing Gao, Gaoge Hu, Shesheng Gao, Rimma Lapovok, Yuri Estrin, Kup‐Sze Choi, Benoît Beausir and Julian A. Smith and has published in prestigious journals such as SHILAP Revista de lepidopterología, Acta Materialia and Materials Science and Engineering A.

In The Last Decade

Chengfan Gu

82 papers receiving 2.3k citations

Peers

Chengfan Gu

Yide Wang France

Jianye Liu China

Somdatta Goswami United States

Xin Zhang China

S. Gopalakrishnan India

Ranjan Ganguli India

Songye Zhu Hong Kong

Seid Korić United States

Mingjun Zhang China

Lu Liu China

Yide Wang France

Chengfan Gu

793 ×1.0

464 ×0.6

167 ×0.2

236 ×0.4

191 ×0.4

Citations per year, relative to Chengfan Gu Chengfan Gu (= 1×) peers Yide Wang

Countries citing papers authored by Chengfan Gu

Since Specialization

Citations

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

Fields of papers citing papers by Chengfan Gu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chengfan Gu

This figure shows the co-authorship network connecting the top 25 collaborators of Chengfan Gu. A scholar is included among the top collaborators of Chengfan Gu 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 Chengfan Gu. Chengfan Gu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Zhu, Xinhe, et al.. (2025). Extended Kalman filter-based maximum likelihood estimation for dynamic soft tissue characterisation. Engineering Applications of Artificial Intelligence. 143. 109965–109965. 1 indexed citations

Zhu, Xinhe, Yongmin Zhong, Kup‐Sze Choi, et al.. (2023). Iterative Kalman filter for biological tissue identification. International Journal of Robust and Nonlinear Control. 35(10). 3949–3961. 14 indexed citations

Zhong, Yongmin, et al.. (2022). Maximum likelihood-based extended Kalman filter for soft tissue modelling. Journal of the mechanical behavior of biomedical materials. 137. 105553–105553. 12 indexed citations

Zhu, Xinhe, Bingbing Gao, Yongmin Zhong, Chengfan Gu, & Kup‐Sze Choi. (2021). Extended Kalman filter based on stochastic epidemiological model for COVID-19 modelling. Computers in Biology and Medicine. 137. 104810–104810. 36 indexed citations

Zhu, Xinhe, Bingbing Gao, Yongmin Zhong, Chengfan Gu, & Kup‐Sze Choi. (2021). Extended Kalman filter for online soft tissue characterization based on Hunt-Crossley contact model. Journal of the mechanical behavior of biomedical materials. 123. 104667–104667. 31 indexed citations

Gao, Bingbing, et al.. (2021). Maximum likelihood-based extended Kalman filter for COVID-19 prediction. Chaos Solitons & Fractals. 146. 110922–110922. 29 indexed citations

Zhong, Yongmin, et al.. (2020). Extended Kalman Filter Nonlinear Finite Element Method for Nonlinear Soft Tissue Deformation. Computer Methods and Programs in Biomedicine. 200. 105828–105828. 26 indexed citations

Shin, Jaehyun, Yongmin Zhong, & Chengfan Gu. (2020). Real-Time Nonlinear Characterization of Soft Tissue Mechanical Properties. Journal of Sensors. 2020. 1–15. 6 indexed citations

Gao, Zhaohui, et al.. (2019). Adaptively Random Weighted Cubature Kalman Filter for Nonlinear Systems. Mathematical Problems in Engineering. 2019(1). 31 indexed citations

10.

Zhong, Yongmin, et al.. (2018). Soft tissue deformation modelling through neural dynamics-based reaction-diffusion mechanics. Medical & Biological Engineering & Computing. 56(12). 2163–2176. 7 indexed citations

11.

Zhong, Yongmin, et al.. (2018). Soft tissue deformation estimation by spatio-temporal Kalman filter finite element method. Technology and Health Care. 26(1_suppl). 317–325. 3 indexed citations

12.

Zhong, Yongmin, et al.. (2018). Neural network modelling of soft tissue deformation for surgical simulation. Artificial Intelligence in Medicine. 97. 61–70. 30 indexed citations

13.

Zhong, Yongmin, et al.. (2017). ChainMail based neural dynamics modeling of soft tissue deformation for surgical simulation. Technology and Health Care. 25(1_suppl). 231–239. 8 indexed citations

14.

Zhong, Yongmin, et al.. (2017). Cellular neural network modelling of soft tissue dynamics for surgical simulation. Technology and Health Care. 25(1_suppl). 337–344. 9 indexed citations

15.

Li, Xin, Yongmin Zhong, Aleksandar Subic, et al.. (2016). Prediction of tissue thermal damage. Technology and Health Care. 24(s2). S625–S629. 9 indexed citations

16.

Li, Xin, Yongmin Zhong, Julian A. Smith, & Chengfan Gu. (2016). Non-Fourier based thermal-mechanical tissue damage prediction for thermal ablation. Bioengineered. 8(1). 71–77. 8 indexed citations

17.

Pardis, N., Cai Chen, R. Ebrahimi, et al.. (2015). Microstructure, texture and mechanical properties of cyclic expansion–extrusion deformed pure copper. Materials Science and Engineering A. 628. 423–432. 55 indexed citations

18.

Tóth, László S. & Chengfan Gu. (2014). Ultrafine-grain metals by severe plastic deformation. Materials Characterization. 92. 1–14. 213 indexed citations

19.

Zhong, Yongmin, et al.. (2011). An improved mass-spring model for soft tissue deformation with haptic feedback. RMIT Research Repository (RMIT University Library). 1–6. 4 indexed citations

20.

Zhong, Yongmin, Bijan Shirinzadeh, Julian A. Smith, & Chengfan Gu. (2009). An electromechanical based deformable model for soft tissue simulation. Artificial Intelligence in Medicine. 47(3). 275–288. 12 indexed citations

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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