Hyunkyoo Cho

737 total citations
43 papers, 566 citations indexed

About

Hyunkyoo Cho is a scholar working on Statistics, Probability and Uncertainty, Computational Theory and Mathematics and Mechanics of Materials. According to data from OpenAlex, Hyunkyoo Cho has authored 43 papers receiving a total of 566 indexed citations (citations by other indexed papers that have themselves been cited), including 37 papers in Statistics, Probability and Uncertainty, 27 papers in Computational Theory and Mathematics and 10 papers in Mechanics of Materials. Recurrent topics in Hyunkyoo Cho's work include Probabilistic and Robust Engineering Design (36 papers), Advanced Multi-Objective Optimization Algorithms (27 papers) and Optimal Experimental Design Methods (10 papers). Hyunkyoo Cho is often cited by papers focused on Probabilistic and Robust Engineering Design (36 papers), Advanced Multi-Objective Optimization Algorithms (27 papers) and Optimal Experimental Design Methods (10 papers). Hyunkyoo Cho collaborates with scholars based in South Korea, United States and China. Hyunkyoo Cho's co-authors include Kyung K. Choi, Ikjin Lee, Weifei Hu, David Lamb, Yongsu Jung, Nicholas J. Gaul, David Gorsich, Young‐Do Choi, Kyung‐Eun Lee and Seok-Heum Baek and has published in prestigious journals such as SAE technical papers on CD-ROM/SAE technical paper series, IEEE Transactions on Magnetics and Structural and Multidisciplinary Optimization.

In The Last Decade

Hyunkyoo Cho

39 papers receiving 560 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Hyunkyoo Cho South Korea 15 398 272 193 129 117 43 566
Nicolas Relun France 5 569 1.4× 286 1.1× 241 1.2× 142 1.1× 139 1.2× 7 642
Harok Bae United States 11 525 1.3× 204 0.8× 265 1.4× 78 0.6× 73 0.6× 44 631
Yicheng Zhou China 13 468 1.2× 266 1.0× 255 1.3× 104 0.8× 82 0.7× 31 657
Beiqing Huang United States 7 626 1.6× 303 1.1× 241 1.2× 139 1.1× 149 1.3× 11 688
Roland Schöbi Switzerland 8 596 1.5× 253 0.9× 312 1.6× 118 0.9× 71 0.6× 14 720
Chunyan Ling China 15 591 1.5× 264 1.0× 272 1.4× 129 1.0× 116 1.0× 39 733
Chuang Xiong China 9 370 0.9× 155 0.6× 218 1.1× 49 0.4× 90 0.8× 12 506
Yishang Zhang China 8 479 1.2× 278 1.0× 196 1.0× 159 1.2× 91 0.8× 10 534
Younès Aoues France 13 476 1.2× 282 1.0× 335 1.7× 74 0.6× 101 0.9× 29 680
Chengning Zhou China 9 384 1.0× 222 0.8× 198 1.0× 105 0.8× 110 0.9× 21 559

Countries citing papers authored by Hyunkyoo Cho

Since Specialization
Citations

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

Fields of papers citing papers by Hyunkyoo Cho

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Hyunkyoo Cho

This figure shows the co-authorship network connecting the top 25 collaborators of Hyunkyoo Cho. A scholar is included among the top collaborators of Hyunkyoo Cho 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 Hyunkyoo Cho. Hyunkyoo Cho 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.
Jung, Yongsu, et al.. (2024). Confidence-based design optimization using multivariate kernel density estimation under insufficient input data. Probabilistic Engineering Mechanics. 78. 103702–103702. 6 indexed citations
2.
Cho, Hyunkyoo, et al.. (2024). Enhancement in Mechanical Properties of AA5052 Sheet for Small Ship by Cold Roll-Bonding Process. Journal of Welding and Joining. 42(5). 550–559.
3.
Hu, Weifei, et al.. (2024). Reliability-based design optimization: a state-of-the-art review of its methodologies, applications, and challenges. Structural and Multidisciplinary Optimization. 67(9). 11 indexed citations
4.
5.
Cho, Hyunkyoo, et al.. (2024). A Numerical Study on the Effects of Liquid Hydrogen Tank Sloshing Parameters and Rib Structure on the Tank Internal Flow. The KSFM Journal of Fluid Machinery. 27(6). 53–61.
6.
Lee, Mingyu, et al.. (2023). An effective active learning strategy for reliability-based design optimization under multiple simulation models. Structural Safety. 107. 102426–102426. 4 indexed citations
7.
Choi, Young‐Do, et al.. (2021). Reduced-dimensional design optimization of stay vane and casing of reaction hydro turbines using global sensitivity analysis. Journal of Mechanical Science and Technology. 35(4). 1487–1499. 3 indexed citations
8.
Jung, Yongsu, et al.. (2021). Confidence-Based Design Optimization for a More Conservative Optimum Under Surrogate Model Uncertainty Caused by Gaussian Process. Journal of Mechanical Design. 143(9). 25 indexed citations
9.
Jung, Yongsu, Hyunkyoo Cho, & Ikjin Lee. (2020). Intelligent initial point selection for MPP search in reliability-based design optimization. Structural and Multidisciplinary Optimization. 62(4). 1809–1820. 16 indexed citations
10.
Jung, Yongsu, Hyunkyoo Cho, & Ikjin Lee. (2019). Reliability measure approach for confidence-based design optimization under insufficient input data. Structural and Multidisciplinary Optimization. 60(5). 1967–1982. 26 indexed citations
11.
Kim, Seung-Jun, Jin‐Hyuk Kim, Won-Gu Joo, et al.. (2019). Establishment of Numerical Analysis Method of Pump-Turbine for Pumped Storage. The KSFM Journal of Fluid Machinery. 22(2). 22–29. 4 indexed citations
12.
Lee, Kyung‐Eun, Hyunkyoo Cho, & Ikjin Lee. (2018). Variable selection using Gaussian process regression-based metrics for high-dimensional model approximation with limited data. Structural and Multidisciplinary Optimization. 59(5). 1439–1454. 23 indexed citations
13.
Cho, Hyunkyoo, et al.. (2018). Confidence-based reliability assessment considering limited numbers of both input and output test data. Structural and Multidisciplinary Optimization. 57(5). 2027–2043. 36 indexed citations
14.
Cho, Hyunkyoo, Kyung K. Choi, Nicholas J. Gaul, et al.. (2016). Conservative reliability-based design optimization method with insufficient input data. Structural and Multidisciplinary Optimization. 54(6). 1609–1630. 44 indexed citations
15.
16.
Choi, Kyung K., et al.. (2015). Development of a Conservative Model Validation Approach for Reliable Analysis. 4 indexed citations
17.
Gaul, Nicholas J., Mary Kathryn Cowles, Hyunkyoo Cho, Kyung K. Choi, & David Lamb. (2015). Modified Bayesian Kriging for Noisy Response Problems for Reliability Analysis. 12 indexed citations
18.
Choi, Myung-Jin, Hyunkyoo Cho, Kyung K. Choi, & Seonho Cho. (2014). Sampling-Based RBDO of Ship Hull Structures Considering Thermo-Elasto-Plastic Residual Deformation. Mechanics Based Design of Structures and Machines. 43(2). 183–208. 15 indexed citations
19.
Cho, Hyunkyoo, et al.. (2013). Reliability-based design optimization of fluid–solid interaction problems. Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science. 228(10). 1724–1742. 6 indexed citations
20.
Cho, Hyunkyoo, et al.. (2012). Confidence Level Estimation and Design Sensitivity Analysis for Confidence-Based RBDO. 1227–1238. 2 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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