Yilang Liu

1.1k total citations · 1 hit paper
30 papers, 870 citations indexed

About

Yilang Liu is a scholar working on Computational Mechanics, Statistical and Nonlinear Physics and Environmental Engineering. According to data from OpenAlex, Yilang Liu has authored 30 papers receiving a total of 870 indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Computational Mechanics, 16 papers in Statistical and Nonlinear Physics and 6 papers in Environmental Engineering. Recurrent topics in Yilang Liu's work include Fluid Dynamics and Turbulent Flows (25 papers), Computational Fluid Dynamics and Aerodynamics (16 papers) and Model Reduction and Neural Networks (16 papers). Yilang Liu is often cited by papers focused on Fluid Dynamics and Turbulent Flows (25 papers), Computational Fluid Dynamics and Aerodynamics (16 papers) and Model Reduction and Neural Networks (16 papers). Yilang Liu collaborates with scholars based in China and United Kingdom. Yilang Liu's co-authors include Weiwei Zhang, Jiaqing Kou, Linyang Zhu, Zhengyin Ye, Xintao Li, Chuanqiang Gao, Yuewen Jiang, Wenbo Cao, Zhenhua Xia and Xianxu Yuan and has published in prestigious journals such as Journal of Fluid Mechanics, AIAA Journal and Physics of Fluids.

In The Last Decade

Yilang Liu

29 papers receiving 849 citations

Hit Papers

Machine learning methods for turbulence modeling in subso... 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
Yilang Liu China 14 733 425 326 128 87 30 870
Maziar S. Hemati United States 11 606 0.8× 461 1.1× 343 1.1× 57 0.4× 80 0.9× 52 874
Stefan Siegel United States 20 998 1.4× 500 1.2× 491 1.5× 135 1.1× 61 0.7× 93 1.3k
Georgios Rigas United Kingdom 18 921 1.3× 260 0.6× 617 1.9× 253 2.0× 106 1.2× 40 1.1k
Olivier Marquet France 18 1.1k 1.5× 253 0.6× 397 1.2× 238 1.9× 59 0.7× 38 1.2k
Jürgen Seidel United States 14 627 0.9× 221 0.5× 441 1.4× 76 0.6× 49 0.6× 115 770
Zhicheng Wang United States 10 431 0.6× 285 0.7× 177 0.5× 100 0.8× 52 0.6× 13 649
Yaomin Zhao China 18 694 0.9× 241 0.6× 345 1.1× 122 1.0× 216 2.5× 51 844
Marek Morzyński Poland 11 989 1.3× 821 1.9× 258 0.8× 141 1.1× 99 1.1× 27 1.2k
Giovanni Stabile Italy 15 460 0.6× 523 1.2× 198 0.6× 61 0.5× 68 0.8× 43 775
A.G. Buchan United Kingdom 12 393 0.5× 441 1.0× 216 0.7× 73 0.6× 46 0.5× 24 704

Countries citing papers authored by Yilang Liu

Since Specialization
Citations

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

Fields of papers citing papers by Yilang Liu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yilang Liu

This figure shows the co-authorship network connecting the top 25 collaborators of Yilang Liu. A scholar is included among the top collaborators of Yilang Liu 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 Yilang Liu. Yilang Liu 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
1.
Liu, Yilang, et al.. (2024). A data assimilation method for recovering turbulent flows using heterogeneous experimental data. Aerospace Science and Technology. 157. 109770–109770. 2 indexed citations
2.
Liu, Yilang, et al.. (2024). Development and deployment of data-driven turbulence model for three-dimensional complex configurations. Machine Learning Science and Technology. 5(3). 35085–35085. 1 indexed citations
3.
Cao, Wenbo, et al.. (2024). A generalized framework for integrating machine learning into computational fluid dynamics. Journal of Computational Science. 82. 102404–102404. 8 indexed citations
4.
Liu, Yilang, et al.. (2024). Neural network-augmented SED-SL modeling of turbulent flows over airfoils. Acta Mechanica Sinica. 40(3).
5.
Cao, Wenbo, et al.. (2023). A novel convergence enhancement method based on online dimension reduction optimization. Physics of Fluids. 35(3). 9 indexed citations
6.
Liu, Yilang, et al.. (2023). Novel Approach to Improve Stability and Convergence of Flowfield Solution Processes: Mode Multigrid. AIAA Journal. 61(8). 3496–3506. 6 indexed citations
7.
Liu, Yilang, Wenbo Cao, Weiwei Zhang, & Zhenhua Xia. (2022). Analysis on numerical stability and convergence of Reynolds averaged Navier–Stokes simulations from the perspective of coupling modes. Physics of Fluids. 34(1). 22 indexed citations
8.
Liu, Yilang, et al.. (2022). Central mean temperature scaling in compressible turbulent channel flows with symmetric isothermal boundaries. Physical Review Fluids. 7(4). 15 indexed citations
9.
Liu, Yilang, Weiwei Zhang, & Zhenhua Xia. (2022). A new data assimilation method of recovering turbulent mean flow field at high Reynolds numbers. Aerospace Science and Technology. 126. 107328–107328. 11 indexed citations
10.
Liu, Yilang, et al.. (2022). Equivalence of three thermal boundary conditions in compressible turbulent channel flows. Physical review. E. 105(6). 65106–65106. 4 indexed citations
11.
Liu, Yilang, et al.. (2021). Improved Mode Multigrid Method for Accelerating Turbulence Flows. AIAA Journal. 59(8). 3012–3024. 2 indexed citations
12.
Zhu, Linyang, Weiwei Zhang, Jiaqing Kou, & Yilang Liu. (2019). Machine learning methods for turbulence modeling in subsonic flows around airfoils. Physics of Fluids. 31(1). 258 indexed citations breakdown →
13.
Liu, Yilang, et al.. (2019). An accuracy preserving limiter for the high-order discontinuous Galerkin method on unstructured grids. Computers & Fluids. 192. 104253–104253. 3 indexed citations
14.
Liu, Yilang, et al.. (2018). Static Aeroelastic Modeling and Rapid Analysis of Wings in Transonic Flow. International Journal of Aerospace Engineering. 2018. 1–12. 4 indexed citations
15.
Gao, Chuanqiang, Weiwei Zhang, Jiaqing Kou, Yilang Liu, & Zhengyin Ye. (2017). Active control of transonic buffet flow. Journal of Fluid Mechanics. 824. 312–351. 66 indexed citations
16.
Gao, Chuanqiang, Weiwei Zhang, Xintao Li, et al.. (2017). Mechanism of frequency lock-in in transonic buffeting flow. Journal of Fluid Mechanics. 818. 528–561. 102 indexed citations
17.
Liu, Yilang & Weiwei Zhang. (2017). Accuracy preserving limiter for the high-order finite volume method on unstructured grids. Computers & Fluids. 149. 88–99. 17 indexed citations
18.
Liu, Yilang, Weiwei Zhang, Yuewen Jiang, & Zhengyin Ye. (2016). A high-order finite volume method on unstructured grids using RBF reconstruction. Computers & Mathematics with Applications. 72(4). 1096–1117. 33 indexed citations
19.
Zhang, Weiwei, Yilang Liu, & Jing Li. (2015). A Simple Strategy for Capturing the Unstable Steady Solution of an Unsteady Flow. Open Journal of Fluid Dynamics. 5(2). 188–198. 5 indexed citations
20.
Zhang, Weiwei, Chuanqiang Gao, Yilang Liu, Zhengyin Ye, & Yuewen Jiang. (2015). The interaction between flutter and buffet in transonic flow. Nonlinear Dynamics. 82(4). 1851–1865. 50 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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