Chang-Ock Lee

466 total citations
44 papers, 293 citations indexed

About

Chang-Ock Lee is a scholar working on Computational Mechanics, Electrical and Electronic Engineering and Mechanics of Materials. According to data from OpenAlex, Chang-Ock Lee has authored 44 papers receiving a total of 293 indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Computational Mechanics, 14 papers in Electrical and Electronic Engineering and 11 papers in Mechanics of Materials. Recurrent topics in Chang-Ock Lee's work include Advanced Numerical Methods in Computational Mathematics (19 papers), Numerical methods in engineering (11 papers) and Electromagnetic Simulation and Numerical Methods (9 papers). Chang-Ock Lee is often cited by papers focused on Advanced Numerical Methods in Computational Mathematics (19 papers), Numerical methods in engineering (11 papers) and Electromagnetic Simulation and Numerical Methods (9 papers). Chang-Ock Lee collaborates with scholars based in South Korea, United States and Russia. Chang-Ock Lee's co-authors include Dongwoo Sheen, Hyea Hyun Kim, Jongwoo Lee, Jooyoung Hahn, Kiwan Jeon, Jong-Ho Park, Hyung Joong Kim, A.R. Hayotov, Eung‐Je Woo and Jongwoo Lee and has published in prestigious journals such as Computer Methods in Applied Mechanics and Engineering, International Journal for Numerical Methods in Engineering and Neurocomputing.

In The Last Decade

Chang-Ock Lee

37 papers receiving 256 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Chang-Ock Lee South Korea 9 141 88 85 64 45 44 293
Antti Hannukainen Finland 12 187 1.3× 121 1.4× 151 1.8× 101 1.6× 31 0.7× 50 428
Uno Hämarik Estonia 12 118 0.8× 65 0.7× 23 0.3× 30 0.5× 93 2.1× 30 384
Gabriele Inglese Italy 9 60 0.4× 187 2.1× 48 0.6× 140 2.2× 99 2.2× 30 431
Anthony Nouy France 9 129 0.9× 104 1.2× 27 0.3× 92 1.4× 35 0.8× 27 441
Hoang Tran United States 12 297 2.1× 37 0.4× 19 0.2× 105 1.6× 49 1.1× 25 408
Robert Luce France 10 207 1.5× 148 1.7× 35 0.4× 107 1.7× 27 0.6× 38 383
Jan Valdman Czechia 11 161 1.1× 143 1.6× 40 0.5× 122 1.9× 85 1.9× 38 341
Irwin Yousept Germany 13 236 1.7× 95 1.1× 71 0.8× 215 3.4× 24 0.5× 42 398
M.C. Rivara Chile 5 182 1.3× 51 0.6× 40 0.5× 36 0.6× 10 0.2× 7 287

Countries citing papers authored by Chang-Ock Lee

Since Specialization
Citations

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

Fields of papers citing papers by Chang-Ock Lee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chang-Ock Lee. 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 Chang-Ock Lee. The network helps show where Chang-Ock Lee may publish in the future.

Co-authorship network of co-authors of Chang-Ock Lee

This figure shows the co-authorship network connecting the top 25 collaborators of Chang-Ock Lee. A scholar is included among the top collaborators of Chang-Ock Lee 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 Chang-Ock Lee. Chang-Ock Lee 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.
Lee, Chang-Ock, et al.. (2025). A nonoverlapping domain decomposition method for extreme learning machines: Elliptic problems. Computers & Mathematics with Applications. 189. 109–128. 2 indexed citations
2.
Lee, Chang-Ock, et al.. (2025). A Neumann-Neumann acceleration with coarse space for domain decomposition of extreme learning machines. Neurocomputing. 655. 131417–131417. 1 indexed citations
3.
Han, Song‐Hee, et al.. (2023). Defect inspection in semiconductor images using FAST-MCD method and neural network. The International Journal of Advanced Manufacturing Technology. 129(3-4). 1547–1565. 3 indexed citations
4.
Lee, Chang-Ock, et al.. (2023). Individual tooth segmentation in human teeth images using pseudo edge-region obtained by deep neural networks. Signal Processing Image Communication. 120. 117076–117076.
5.
Park, Jong-Ho, et al.. (2022). Two-level group convolution. Neural Networks. 154. 323–332. 7 indexed citations
6.
Lee, Chang-Ock & Jong-Ho Park. (2020). RECENT ADVANCES IN DOMAIN DECOMPOSITION METHODS FOR TOTAL VARIATION MINIMIZATION. Journal of the Korea Society for Industrial and Applied Mathematics. 24(2). 161–197. 1 indexed citations
7.
Lee, Chang-Ock & Jong-Ho Park. (2019). Fast Nonoverlapping Block Jacobi Method for the Dual Rudin--Osher--Fatemi Model. SIAM Journal on Imaging Sciences. 12(4). 2009–2034. 4 indexed citations
8.
Lee, Chang-Ock, et al.. (2015). Accurate Surface Reconstruction in 3D Using Two-dimensional Parallel Cross Sections. Journal of Mathematical Imaging and Vision. 53(2). 182–195. 10 indexed citations
9.
Lee, Chang-Ock, Jong-Ho Lee, Hyenkyun Woo, & Sangwoon Yun. (2015). Block Decomposition Methods for Total Variation by Primal–Dual Stitching. Journal of Scientific Computing. 68(1). 273–302. 6 indexed citations
10.
Lee, Chang-Ock, et al.. (2013). A DUAL ITERATIVE SUBSTRUCTURING METHOD WITH AN OPTIMIZED PENALTY PARAMETER. 8(1). 153–154.
11.
Lee, Chang-Ock, et al.. (2012). A dual iterative substructuring method with a penalty term in three dimensions. Computers & Mathematics with Applications. 64(9). 2787–2805. 1 indexed citations
12.
Kim, Hyea Hyun, et al.. (2010). On the selection of primal unknowns for a FETI-DP formulation of the Stokes problem in two dimensions. Computers & Mathematics with Applications. 60(12). 3047–3057. 4 indexed citations
13.
Kim, Hyea Hyun & Chang-Ock Lee. (2010). A FETI–DP Formulation for the Three-Dimensional Stokes Problem without Primal Pressure Unknowns. SIAM Journal on Scientific Computing. 32(6). 3301–3322. 6 indexed citations
14.
Jeon, Kiwan, et al.. (2009). CoReHA: conductivity reconstructor using harmonic algorithms for magnetic resonance electrical impedance tomography (MREIT). Journal of Biomedical Engineering Research. 30(4). 279–287. 28 indexed citations
15.
Hahn, Jooyoung & Chang-Ock Lee. (2009). A Nonlinear Structure Tensor with the Diffusivity Matrix Composed of the Image Gradient. Journal of Mathematical Imaging and Vision. 34(2). 137–151. 15 indexed citations
16.
Kim, Hyea Hyun, et al.. (2006). Preconditioners for the dual-primal FETI methods on nonmatching grids: Numerical study. Computers & Mathematics with Applications. 51(5). 697–712. 4 indexed citations
17.
Lee, Chang-Ock, Jongwoo Lee, & Dongwoo Sheen. (2003). A Locking-Free Nonconforming Finite Element Method for Planar Linear Elasticity. Advances in Computational Mathematics. 19(1-3). 277–291. 44 indexed citations
18.
Lee, Chang-Ock, Jongwoo Lee, & Dongwoo Sheen. (2002). A FREQUENCY-DOMAIN METE10D FOR FINITE ELEMENT SOLUTIONS OF PARABOLIC PROBLEMS. Bulletin of the Korean Mathematical Society. 39(4). 589–606. 1 indexed citations
19.
Lee, Chang-Ock, Jongwoo Lee, Dongwoo Sheen, & Yongjin Yeom. (1999). A frequency-domain parallel method for the numerical approximation of parabolic problems. Computer Methods in Applied Mechanics and Engineering. 169(1-2). 19–29. 11 indexed citations
20.
Lee, Chang-Ock. (1998). A conforming mixed finite element method for the pure traction problem of linear elasticity. Applied Mathematics and Computation. 93(1). 11–29. 8 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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