Fangying Song

1.0k citations
25 papers · 643 indexed · 1 hit paper · h-index 11
Topics
Fractional Differential Equations Solutions (8 papers)Differential Equations and Numerical Methods (7 papers)Advanced Mathematical Modeling in Engineering (4 papers)
Partner nations
ChinaUnited StatesTaiwan

In The Last Decade

Fangying Song

24 papers receiving 614 citations

Hit Papers

What is the fractional Laplacian? A comparative review wi...2019202620212023201950100150200250

Peers

Fangying Song
Comparison fields: 5 of 96
  • Modeling and Simulation 305
  • Numerical Analysis 228
  • Applied Mathematics 124
  • Statistical and Nonlinear Physics 114
  • Computational Mechanics 108
Replace Christian Glusa with:
Christian Glusa United States
Guofei Pang China
Nicholas Hale United Kingdom
Mamikon Gulian United States
S. Mohammad Hosseini Iran
Ghasem Barid Loghmani Iran
Mani Mehra India
Hojatollah Adibi Iran
M.R. Eslahchi Iran
Marina Popolizio Italy
Fangying Song relative to Christian Glusa United States Christian Glusa's profile →
Citations per field
00.5×10×13×
Christian Glusa · 1×
Citations per year

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
#WorkIndexed citations
1 0
2 1
3 3
4 2
5 1
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7 5
8 7
9 20
10 8
11 11
12
Fractional physical-inform neural networks (fPINNs) for turbulent flows
5
13 67
14 37
15 10
16 3
17 34
18 4
19 77
20 39

About Fangying Song

Fangying Song is a scholar working on Modeling and Simulation, Numerical Analysis and Statistical and Nonlinear Physics, having authored 25 papers that have together received 643 indexed citations. Recurring topics across this work include Fractional Differential Equations Solutions (8 papers), Differential Equations and Numerical Methods (7 papers) and Advanced Mathematical Modeling in Engineering (4 papers). The work is most often cited by research in Modeling and Simulation (305 citations), Numerical Analysis (228 citations) and Applied Mathematics (124 citations). Fangying Song has collaborated with scholars based in China, United States and Taiwan. Frequent co-authors include George Em Karniadakis, Chuanju Xu, Guofei Pang, Zhiping Mao, Anna Lischke, Xiaoning Zheng, Wei Cai, Mark M. Meerschaert, Mark Ainsworth and Mamikon Gulian. Their work appears in journals such as Journal of Computational Physics, Computer Methods in Applied Mechanics and Engineering and International Journal of Environmental Research and Public Health.

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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