Fangying Song

1.0k total citations · 1 hit paper
25 papers, 643 citations indexed

About

Fangying Song is a scholar working on Numerical Analysis, Modeling and Simulation and Statistical and Nonlinear Physics. According to data from OpenAlex, Fangying Song has authored 25 papers receiving a total of 643 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Numerical Analysis, 8 papers in Modeling and Simulation and 6 papers in Statistical and Nonlinear Physics. Recurrent topics in Fangying Song's work include Fractional Differential Equations Solutions (8 papers), Differential Equations and Numerical Methods (7 papers) and Advanced Mathematical Modeling in Engineering (4 papers). Fangying Song is often cited by papers focused on Fractional Differential Equations Solutions (8 papers), Differential Equations and Numerical Methods (7 papers) and Advanced Mathematical Modeling in Engineering (4 papers). Fangying Song collaborates with scholars based in China, United States and Taiwan. Fangying Song's co-authors include George Em Karniadakis, Chuanju Xu, Guofei Pang, Zhiping Mao, Mamikon Gulian, Xiaoning Zheng, Wei Cai, Mark M. Meerschaert, Christian Glusa and Anna Lischke and has published in prestigious journals such as Journal of Computational Physics, Computer Methods in Applied Mechanics and Engineering and International Journal of Environmental Research and Public Health.

In The Last Decade

Fangying Song

24 papers receiving 614 citations

Hit Papers

What is the fractional La... 2019 2026 2021 2023 2019 50 100 150 200 250

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Fangying Song China 11 305 228 124 114 108 25 643
Guofei Pang China 12 371 1.2× 226 1.0× 121 1.0× 219 1.9× 117 1.1× 25 738
Christian Glusa United States 9 246 0.8× 182 0.8× 102 0.8× 97 0.9× 117 1.1× 21 544
Mamikon Gulian United States 7 166 0.5× 99 0.4× 91 0.7× 111 1.0× 65 0.6× 16 410
Nicholas Hale United Kingdom 14 236 0.8× 337 1.5× 177 1.4× 76 0.7× 140 1.3× 28 905
Xian‐Ming Gu China 17 607 2.0× 652 2.9× 110 0.9× 160 1.4× 109 1.0× 81 1.0k
Ghasem Barid Loghmani Iran 16 530 1.7× 448 2.0× 166 1.3× 93 0.8× 125 1.2× 78 841
Xuan Zhao China 16 610 2.0× 503 2.2× 120 1.0× 179 1.6× 55 0.5× 59 965
G. Hariharan India 20 643 2.1× 390 1.7× 103 0.8× 282 2.5× 51 0.5× 69 954
Roman Wituła Poland 11 173 0.6× 121 0.5× 79 0.6× 129 1.1× 28 0.3× 79 484
Bruce A. Wade United States 13 114 0.4× 263 1.2× 71 0.6× 55 0.5× 148 1.4× 48 547

Countries citing papers authored by Fangying Song

Since Specialization
Citations

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

Fields of papers citing papers by Fangying Song

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fangying Song

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

All Works

20 of 20 papers shown
2.
Zhang, Jun, Fangying Song, Xiaofeng Yang, & Yu Zhang. (2024). Error analysis of the explicit-invariant energy quadratization (EIEQ) numerical scheme for solving the Allen–Cahn equation. Journal of Computational and Applied Mathematics. 457. 116224–116224. 1 indexed citations
3.
Song, Fangying, et al.. (2024). Fourier Neural Operator Networks for Solving Reaction–Diffusion Equations. Fluids. 9(11). 258–258. 2 indexed citations
4.
Song, Fangying, Xiaowei Zhong, Ju Zhang, et al.. (2024). Characteristics of bacterial community in eyelashes of patients with Demodex blepharitis. Parasites & Vectors. 17(1). 64–64. 3 indexed citations
5.
Chang, Jou–Ming, et al.. (2023). All-to-All Broadcast Algorithm in Galaxyfly Networks. Mathematics. 11(11). 2459–2459. 1 indexed citations
6.
Song, Fangying, et al.. (2023). Scalar auxiliary variable approache for the surface quasi-geostrophic equation. Journal of Algorithms & Computational Technology. 17. 2 indexed citations
7.
Hwang, Feng-Jang, et al.. (2022). Machine learning and deep learning methods for wireless network applications. EURASIP Journal on Wireless Communications and Networking. 2022(1). 5 indexed citations
8.
Song, Fangying & George Em Karniadakis. (2021). Variable-Order Fractional Models for Wall-Bounded Turbulent Flows. Entropy. 23(6). 782–782. 7 indexed citations
9.
Jia, Huanhuan, et al.. (2021). Nurses’ Occupational Stress and Presenteeism: The Mediating Role of Public Service Motivation and the Moderating Role of Health. International Journal of Environmental Research and Public Health. 18(7). 3523–3523. 20 indexed citations
10.
Li, Xianjuan, Zhiping Mao, Nan Wang, et al.. (2020). A fast solver for spectral elements applied to fractional differential equations using hierarchical matrix approximation. Computer Methods in Applied Mechanics and Engineering. 366. 113053–113053. 11 indexed citations
11.
Li, Qi & Fangying Song. (2020). Splitting spectral element method for fractional reaction-diffusion equations. Journal of Algorithms & Computational Technology. 14. 8 indexed citations
12.
Song, Fangying, et al.. (2019). Fractional physical-inform neural networks (fPINNs) for turbulent flows. Bulletin of the American Physical Society. 5 indexed citations
13.
Chen, Chi‐Hua, Fangying Song, Feng-Jang Hwang, & Ling Wu. (2019). A probability density function generator based on neural networks. Physica A Statistical Mechanics and its Applications. 541. 123344–123344. 67 indexed citations
14.
Pang, Guofei, et al.. (2019). Discovering a universal variable-order fractional model for turbulent Couette flow using a physics-informed neural network.. Fractional Calculus and Applied Analysis. 22(6). 1675–1688. 37 indexed citations
15.
Liu, Xiaoling, et al.. (2019). An Efficient Spectral Method for the Inextensible Immersed Interface in Incompressible Flows. Communications in Computational Physics. 25(4). 1071–1096. 10 indexed citations
16.
Wang, Tingting, Fangying Song, Hong Wang, & George Em Karniadakis. (2019). Fractional Gray–Scott model: Well-posedness, discretization, and simulations. Computer Methods in Applied Mechanics and Engineering. 347. 1030–1049. 34 indexed citations
17.
Song, Fangying, et al.. (2019). On Parker instability under L2-norm. Nonlinear Analysis. 192. 111697–111697. 3 indexed citations
18.
Song, Fangying, Chuanju Xu, & George Em Karniadakis. (2017). Computing Fractional Laplacians on Complex-Geometry Domains: Algorithms and Simulations. SIAM Journal on Scientific Computing. 39(4). A1320–A1344. 34 indexed citations
19.
Song, Fangying, Fanhai Zeng, Wei Cai, Wen Chen, & George Em Karniadakis. (2016). Efficient two-dimensional simulations of the fractional Szabo equation with different time-stepping schemes. Computers & Mathematics with Applications. 73(6). 1286–1297. 4 indexed citations
20.
Song, Fangying & Chuanju Xu. (2015). Spectral direction splitting methods for two-dimensional space fractional diffusion equations. Journal of Computational Physics. 299. 196–214. 39 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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