Jialing Le

2.3k total citations
110 papers, 1.9k citations indexed

About

Jialing Le is a scholar working on Computational Mechanics, Aerospace Engineering and Fluid Flow and Transfer Processes. According to data from OpenAlex, Jialing Le has authored 110 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 95 papers in Computational Mechanics, 79 papers in Aerospace Engineering and 11 papers in Fluid Flow and Transfer Processes. Recurrent topics in Jialing Le's work include Computational Fluid Dynamics and Aerodynamics (75 papers), Combustion and flame dynamics (45 papers) and Rocket and propulsion systems research (28 papers). Jialing Le is often cited by papers focused on Computational Fluid Dynamics and Aerodynamics (75 papers), Combustion and flame dynamics (45 papers) and Rocket and propulsion systems research (28 papers). Jialing Le collaborates with scholars based in China and United States. Jialing Le's co-authors include Ye Tian, Fuyu Zhong, Shunhua Yang, Mingming Guo, Yuhui Wang, Wen Shi, Hua Zhang, Ran Wei, Chenlin Zhang and Hua Zhang and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of Computational Physics and International Journal of Hydrogen Energy.

In The Last Decade

Jialing Le

104 papers receiving 1.8k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jialing Le China 25 1.5k 1.3k 184 155 148 110 1.9k
Ye Tian China 25 1.6k 1.1× 1.2k 0.9× 51 0.3× 182 1.2× 122 0.8× 127 1.9k
В. Б. Бетелин Russia 14 1.2k 0.8× 1.1k 0.9× 253 1.4× 149 1.0× 337 2.3× 64 1.9k
Ralf Deiterding United Kingdom 26 1.3k 0.8× 1.4k 1.1× 785 4.3× 73 0.5× 396 2.7× 120 2.0k
Zun Cai China 27 1.6k 1.1× 1.2k 1.0× 56 0.3× 218 1.4× 115 0.8× 65 1.9k
Jiajian Zhu China 23 1.1k 0.7× 860 0.7× 58 0.3× 192 1.2× 142 1.0× 68 1.5k
Hongbo Wang China 36 3.8k 2.5× 2.5k 1.9× 115 0.6× 374 2.4× 149 1.0× 153 4.1k
Warren C. Strahle United States 21 1.2k 0.8× 891 0.7× 171 0.9× 337 2.2× 214 1.4× 127 1.6k
Li Yan China 43 4.2k 2.8× 3.4k 2.6× 57 0.3× 215 1.4× 87 0.6× 97 4.8k
Lars-Erik Eriksson Sweden 26 1.8k 1.2× 1.9k 1.5× 821 4.5× 175 1.1× 318 2.1× 121 2.4k
William H. Heiser United States 15 1.5k 1.0× 1.8k 1.4× 267 1.5× 184 1.2× 260 1.8× 47 2.4k

Countries citing papers authored by Jialing Le

Since Specialization
Citations

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

Fields of papers citing papers by Jialing Le

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jialing Le

This figure shows the co-authorship network connecting the top 25 collaborators of Jialing Le. A scholar is included among the top collaborators of Jialing Le 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 Jialing Le. Jialing Le 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.
Tian, Ye, et al.. (2025). Artificial intelligence in scramjet research: Applications and future outlook. Chinese Journal of Aeronautics. 39(4). 103695–103695. 1 indexed citations
2.
Guo, Mingming, Ye Tian, Yi Zhang, et al.. (2025). Fast prediction of flow field in scramjet combustor based on physical information neural network under wide Mach number. Chinese Journal of Aeronautics. 38(7). 103482–103482. 2 indexed citations
3.
Tian, Ye, et al.. (2025). Investigation of self-circulation controlled mechanism on combustion characteristics in a scramjet combustor. Applied Thermal Engineering. 280. 128207–128207. 1 indexed citations
4.
Tian, Ye, et al.. (2025). Research on intelligent prediction method of supersonic flow field in scramjet based on deep learning: A review. Expert Systems with Applications. 279. 127500–127500. 2 indexed citations
5.
Tian, Ye, et al.. (2025). Investigating the combustion-flow coupling effects in a cavity-based scramjet combustor. Combustion and Flame. 282. 114485–114485. 1 indexed citations
6.
Zhong, Zuliang, Ye Tian, Wenyan Song, & Jialing Le. (2025). Hybrid physics-machine learning framework for mathematical modeling of supersonic combustion mode transitions across wide speed range. Knowledge-Based Systems. 322. 113773–113773. 1 indexed citations
7.
8.
Deng, Xue, et al.. (2025). Multi-objective optimization of scramjet nozzle under geometric constraints using a deep learning method. Aerospace Science and Technology. 164. 110403–110403. 1 indexed citations
9.
Tian, Ye, et al.. (2025). Suppression of combustion instability in a scramjet engine using unsteady fuel injection. Acta Astronautica. 237. 12–22. 2 indexed citations
10.
Zhang, Bin, et al.. (2025). Time-space adaptive implicit-explicit method for unsteady detonations. Journal of Computational Physics. 540. 114266–114266.
11.
Le, Jialing, et al.. (2024). Progress and prospects of artificial intelligence development and applications in supersonic flow and combustion. Progress in Aerospace Sciences. 151. 101046–101046. 11 indexed citations
12.
Guo, Mingming, Ye Tian, Linjing Li, et al.. (2024). Supersonic combustion flow field reconstruction based on multi-view domain adaptation generative network in scramjet combustor. Engineering Applications of Artificial Intelligence. 136. 108981–108981. 4 indexed citations
13.
Guo, Mingming, et al.. (2024). Supersonic combustion field evolution prediction in scramjet engine using a deblurring multi-scale attention network. Expert Systems with Applications. 252. 124290–124290. 5 indexed citations
14.
Liu, Haochen, et al.. (2024). Large eddy simulation of lean blow-off in swirl-stabilized flame with the subgrid dissipation concept. Combustion and Flame. 267. 113596–113596. 3 indexed citations
15.
Guo, Mingming, et al.. (2024). Hypersonic inlet flow field reconstruction dominated by shock wave and boundary layer based on small sample physics-informed neural networks. Aerospace Science and Technology. 150. 109205–109205. 12 indexed citations
16.
Tian, Ye, et al.. (2023). Intelligent reconstruction algorithm of hydrogen-fueled scramjet combustor flow based on knowledge distillation model compression. International Journal of Hydrogen Energy. 49. 1278–1291. 15 indexed citations
17.
Guo, Mingming, et al.. (2023). Uncertainty quantification and identification of SST turbulence model parameters based on Bayesian optimization algorithm in supersonic flow. International Journal for Numerical Methods in Fluids. 96(3). 277–296. 7 indexed citations
18.
Guo, Mingming, et al.. (2023). Flame reconstruction of hydrogen fueled-scramjet combustor based on multi-source information fusion. International Journal of Hydrogen Energy. 48(80). 31350–31365. 10 indexed citations
19.
Guo, Mingming, et al.. (2023). Flow field reconstruction in inlet of scramjet at Mach 10 based on physical information neural network. Physics of Fluids. 35(10). 9 indexed citations
20.
Man, Teng, Jialing Le, Mihai Marasteanu, & K. M. Hill. (2021). Two-Scale Discrete Element Modeling of Gyratory Compaction of Hot Asphalt. Journal of Engineering Mechanics. 148(2). 14 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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