Namwoo Kang

1.9k total citations · 1 hit paper
62 papers, 1.2k citations indexed

About

Namwoo Kang is a scholar working on Automotive Engineering, Electrical and Electronic Engineering and Mechanical Engineering. According to data from OpenAlex, Namwoo Kang has authored 62 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Automotive Engineering, 15 papers in Electrical and Electronic Engineering and 9 papers in Mechanical Engineering. Recurrent topics in Namwoo Kang's work include Electric Vehicles and Infrastructure (13 papers), Transportation and Mobility Innovations (10 papers) and Manufacturing Process and Optimization (8 papers). Namwoo Kang is often cited by papers focused on Electric Vehicles and Infrastructure (13 papers), Transportation and Mobility Innovations (10 papers) and Manufacturing Process and Optimization (8 papers). Namwoo Kang collaborates with scholars based in South Korea, United States and Canada. Namwoo Kang's co-authors include Ikjin Lee, Panos Y. Papalambros, Seongsin Kim, Yongsu Jung, Soyoung Yoo, Sangeun Oh, Fred M. Feinberg, Alparslan Emrah Bayrak, Seowoo Jang and Sung‐Hee Lee and has published in prestigious journals such as Journal of Cleaner Production, Expert Systems with Applications and Computer Methods in Applied Mechanics and Engineering.

In The Last Decade

Namwoo Kang

55 papers receiving 1.2k citations

Hit Papers

Deep Generative Design: Integration of Topology Optimizat... 2019 2026 2021 2023 2019 50 100 150 200 250
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Deep Generative Design: Integration of Topology Optimization and Generative Models
    2019 292 citations Yongsu Jung, Seongsin Kim et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Namwoo Kang South Korea 18 366 278 247 230 219 62 1.2k
Kikuo FUJITA Japan 20 106 0.3× 591 2.1× 308 1.2× 414 1.8× 148 0.7× 155 1.4k
Lotfi Romdhane Tunisia 24 248 0.7× 570 2.1× 162 0.7× 209 0.9× 61 0.3× 153 2.2k
Ėric Châtelet France 26 172 0.5× 295 1.1× 348 1.4× 278 1.2× 140 0.6× 92 2.2k
Hongfu Zuo China 20 213 0.6× 495 1.8× 140 0.6× 173 0.8× 321 1.5× 163 1.7k
Eduard Petlenkov Estonia 25 289 0.8× 172 0.6× 57 0.2× 92 0.4× 697 3.2× 192 2.4k
Faez Ahmed United States 16 73 0.2× 301 1.1× 179 0.7× 154 0.7× 26 0.1× 73 1.1k
Yan Fu China 18 125 0.3× 205 0.7× 218 0.9× 53 0.2× 63 0.3× 94 1.0k
Yuanjun Guo China 26 540 1.5× 234 0.8× 115 0.5× 85 0.4× 1.1k 4.9× 100 2.1k
Jizhuang Hui China 16 114 0.3× 228 0.8× 133 0.5× 240 1.0× 106 0.5× 71 1.0k
David S. Stargel United States 8 106 0.3× 348 1.3× 186 0.8× 1.0k 4.4× 173 0.8× 9 1.8k

Countries citing papers authored by Namwoo Kang

Since Specialization
Citations

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

Fields of papers citing papers by Namwoo Kang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Namwoo Kang

This figure shows the co-authorship network connecting the top 25 collaborators of Namwoo Kang. A scholar is included among the top collaborators of Namwoo Kang 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 Namwoo Kang. Namwoo Kang 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.
Kang, Namwoo, et al.. (2025). Data-efficient deep operator network for unsteady flow: A multi-fidelity approach with physics-guided subsampling. Computer Methods in Applied Mechanics and Engineering. 446. 118254–118254. 1 indexed citations
2.
Lee, Sumin & Namwoo Kang. (2025). Vehicle suspension recommendation system: multi-fidelity neural network-based mechanism design optimization. Structural and Multidisciplinary Optimization. 68(3). 2 indexed citations
3.
Yu, Yinghui, et al.. (2025). Physics-constrained graph neural networks for spatio-temporal prediction of drop impact on OLED display panels. Expert Systems with Applications. 274. 126907–126907. 3 indexed citations
4.
Kim, Hojin, et al.. (2024). Data-driven physics-informed neural networks: A digital twin perspective. Computer Methods in Applied Mechanics and Engineering. 428. 117075–117075. 27 indexed citations
5.
Kang, Namwoo, et al.. (2024). Weighted unsupervised domain adaptation considering geometry features and engineering performance of 3D design data. Expert Systems with Applications. 256. 124928–124928. 1 indexed citations
6.
Lee, Sumin, et al.. (2024). Deep generative model-based synthesis framework of four-bar linkage mechanisms with target conditions. Journal of Computational Design and Engineering. 11(5). 318–332. 1 indexed citations
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Kang, Namwoo, et al.. (2023). Shared autonomous electric vehicle system design and optimization under dynamic battery degradation considering varying load conditions. Journal of Cleaner Production. 423. 138795–138795. 3 indexed citations
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Yoo, Soyoung, et al.. (2023). Wheel impact test by deep learning: prediction of location and magnitude of maximum stress. Structural and Multidisciplinary Optimization. 66(1). 10 indexed citations
12.
Kang, Namwoo, et al.. (2023). Design-oriented study on target station for spallation neutron source at KOMAC. Journal of the Korean Physical Society. 83(2). 91–95. 1 indexed citations
13.
Kang, Namwoo, et al.. (2023). Adaptive neural network ensemble using prediction frequency. Journal of Computational Design and Engineering. 10(4). 1547–1560. 4 indexed citations
14.
Kang, Namwoo, et al.. (2023). Topology optimization via machine learning and deep learning: a review. Journal of Computational Design and Engineering. 10(4). 1736–1766. 71 indexed citations
15.
Yoo, Soyoung, et al.. (2021). Effect of Robo-Taxi User Experience on User Acceptance: Field Test Data Analysis. Transportation Research Record Journal of the Transportation Research Board. 2676(2). 350–366. 10 indexed citations
16.
Jung, Yongsu, et al.. (2020). Probabilistic analytical target cascading using kernel density estimation for accurate uncertainty propagation. Structural and Multidisciplinary Optimization. 61(5). 2077–2095. 13 indexed citations
17.
Papalambros, Panos Y., et al.. (2018). Influence of automobile seat form and comfort rating on willingness-to-pay. International Journal of Vehicle Design. 75(1/2/3/4). 75–75. 4 indexed citations
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19.
D’Souza, Kiran, Alparslan Emrah Bayrak, Namwoo Kang, et al.. (2016). An integrated design approach for evaluating the effectiveness and cost of a fleet. The Journal of Defense Modeling and Simulation Applications Methodology Technology. 13(4). 381–397. 6 indexed citations
20.
D’Souza, Kiran, Alparslan Emrah Bayrak, Namwoo Kang, et al.. (2015). An Integrated Design Approach for Evaluating the Utility and Cost of a Fleet. 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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