Youngjoon Hong

2.9k total citations
55 papers, 336 citations indexed

About

Youngjoon Hong is a scholar working on Computational Mechanics, Statistical and Nonlinear Physics and Computational Theory and Mathematics. According to data from OpenAlex, Youngjoon Hong has authored 55 papers receiving a total of 336 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Computational Mechanics, 11 papers in Statistical and Nonlinear Physics and 9 papers in Computational Theory and Mathematics. Recurrent topics in Youngjoon Hong's work include Advanced Numerical Methods in Computational Mathematics (9 papers), Advanced Mathematical Modeling in Engineering (9 papers) and Fluid Dynamics and Turbulent Flows (8 papers). Youngjoon Hong is often cited by papers focused on Advanced Numerical Methods in Computational Mathematics (9 papers), Advanced Mathematical Modeling in Engineering (9 papers) and Fluid Dynamics and Turbulent Flows (8 papers). Youngjoon Hong collaborates with scholars based in United States, South Korea and China. Youngjoon Hong's co-authors include Chang‐Yeol Jung, David P. Nicholls, Roger Témam, Namjung Kim, Huaiyu Tian, Bingying Li, Nils Chr. Stenseth, Chieh‐Hsi Wu, Pai Zheng and Jun‐Ho Choi and has published in prestigious journals such as SHILAP Revista de lepidopterología, Applied Physics Letters and Scientific Reports.

In The Last Decade

Youngjoon Hong

48 papers receiving 319 citations

Peers

Youngjoon Hong

SNP

CTM

Shafaq Naz Pakistan

Charles C. Margossian United States

Dinesh Kumar India

Hui Cao China

Alex Viguerie United States

Iftikhar Ahmad Pakistan

Davide Baroli Germany

Vinicius Albani Brazil

Kwang Ik Kim South Korea

Yannick Privat France

Shafaq Naz Pakistan

Youngjoon Hong

128 ×1.6

87 ×1.2

34 ×0.7

SNP

32 ×0.7

CTM

8 ×0.2

Citations per year, relative to Youngjoon Hong Youngjoon Hong (= 1×) peers Shafaq Naz

Countries citing papers authored by Youngjoon Hong

Since Specialization

Citations

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

Fields of papers citing papers by Youngjoon Hong

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Youngjoon Hong

This figure shows the co-authorship network connecting the top 25 collaborators of Youngjoon Hong. A scholar is included among the top collaborators of Youngjoon Hong 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 Youngjoon Hong. Youngjoon Hong 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

Hong, Youngjoon, et al.. (2025). Finite Element Operator Network for Solving Elliptic-Type Parametric PDEs. SIAM Journal on Scientific Computing. 47(2). C501–C528.

Hong, Youngjoon, et al.. (2025). Physically interpretable discrete latent representations for the design of advanced mechanical metamaterials in complex geometries. Engineering Applications of Artificial Intelligence. 154. 111011–111011. 3 indexed citations

Hong, Youngjoon, et al.. (2025). Singular layer physics-informed neural network method for convection-dominated boundary layer problems in two dimensions. Journal of Computational and Applied Mathematics. 474. 116918–116918.

Shen, Ying, Youngjoon Hong, Thomas Krafft, & Quanyi Wang. (2025). Progress and challenges in infectious disease surveillance and early warning. Research Publications (Maastricht University). 2(1). 100071–100071. 7 indexed citations

Lee, Dong-Chan, et al.. (2025). Forward and inverse simulation of pseudo-two-dimensional model of lithium-ion batteries using neural networks. Computer Methods in Applied Mechanics and Engineering. 438. 117856–117856. 1 indexed citations

Hong, Youngjoon, et al.. (2024). Semi-analytic PINN methods for boundary layer problems in a rectangular domain. Journal of Computational and Applied Mathematics. 450. 115989–115989.

Hong, Youngjoon, et al.. (2024). Deep Neural Network for Solving Differential Equations Motivated by Legendre-Galerkin Approximation. 21(5). 652–673. 1 indexed citations

He, Yufei, et al.. (2024). Deep Reinforcement Learning Algorithm with Long Short-Term Memory Network for Optimizing Unmanned Aerial Vehicle Information Transmission. Mathematics. 13(1). 46–46. 2 indexed citations

Kim, S. K., et al.. (2024). A novel physics-aware graph network using high-order numerical methods in weather forecasting model. Knowledge-Based Systems. 300. 112158–112158. 5 indexed citations

10.

Hong, Youngjoon, et al.. (2024). An adaptive dual-level reinforcement learning approach for optimal trade execution. Expert Systems with Applications. 252. 124263–124263. 1 indexed citations

11.

Chang, Tengyuan, et al.. (2024). Singular layer physics informed neural network method for plane parallel flows. Computers & Mathematics with Applications. 166. 91–105. 1 indexed citations

12.

Wang, Lin, Bingying Li, Youngjoon Hong, et al.. (2023). Marginal effects of public health measures and COVID-19 disease burden in China: A large-scale modelling study. PLoS Computational Biology. 19(9). e1011492–e1011492.

13.

Wang, Ligui, Hui Chen, Shaofu Qiu, et al.. (2023). Search-engine-based surveillance using artificial intelligence for early detection of coronavirus disease outbreak. Journal Of Big Data. 10(1). 1 indexed citations

14.

Wang, Ruixue, Bingying Li, Yidan Li, et al.. (2023). Optimization of Population-Level Testing, Contact Tracing, and Isolation in Emerging COVID-19 Outbreaks: a Mathematical Modeling Study — Tonghua City and Beijing Municipality, China, 2021–2022. China CDC Weekly. 5(4). 82–89. 1 indexed citations

15.

Tian, Huaiyu, Moritz U. G. Kraemer, Youngjoon Hong, et al.. (2022). Malaria elimination on Hainan Island despite climate change. SHILAP Revista de lepidopterología. 2(1). 12–12. 6 indexed citations

16.

Conrad, William H., et al.. (2022). Deep learning forecasting using time-varying parameters of the SIRD model for Covid-19. Scientific Reports. 12(1). 3030–3030. 24 indexed citations

17.

Wu, Xiaomin, Zhenyu He, Yating Wu, et al.. (2021). A follow-up study shows that recovered patients with re-positive PCR test in Wuhan may not be infectious. BMC Medicine. 19(1). 77–77. 18 indexed citations

18.

Hong, Youngjoon, Matthew Otten, Misun Min, Stephen K. Gray, & David P. Nicholls. (2019). Periodic corrugations to increase efficiency of thermophotovoltaic emitting structures. Applied Physics Letters. 114(5). 3 indexed citations

19.

Hong, Youngjoon & D. Wirosoetisno. (2015). Timestepping schemes for the 3d Navier–Stokes equations. Applied Numerical Mathematics. 96. 153–164. 1 indexed citations

20.

Hong, Youngjoon. (2014). Numerical Approximation of the Singularly Perturbed Heat Equation in a Circle. Journal of Scientific Computing. 62(1). 1–24. 11 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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