Tae-Wook Ko

15 papers receiving 570 citations

Peers

Tae-Wook Ko
Comparison fields: 5 of 60
  • Statistical and Nonlinear Physics 180
  • Computer Networks and Communications 287
  • Cognitive Neuroscience 206
  • Materials Chemistry 176
  • Electrical and Electronic Engineering 143
Replace R. Castanedo Pérez with:
R. Castanedo Pérez Mexico
Maosheng Wang China
Konrad Schönleber Germany
Jinming Luo China
Gang Dou China
Davud V. Guseinov Russia
Carlos A.S. Batista Brazil
Jiajie Ying China
Xiaobin Wang China
Tae-Wook Ko relative to R. Castanedo Pérez Mexico R. Castanedo Pérez's profile →
Citations per field
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R. Castanedo Pérez · 1×
Citations per year

Countries citing papers authored by Tae-Wook Ko

Since Specialization
Citations

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

Fields of papers citing papers by Tae-Wook Ko

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tae-Wook Ko. 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 Tae-Wook Ko. The network helps show where Tae-Wook Ko may publish in the future.

Co-authors

The 24 scholars most cited alongside Tae-Wook Ko, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Tae-Wook Ko Line = papers co-authored together Tae-Wook Ko links everyone, so they are left out of the graph.

All Works

18 of 18 papers shown
#Work
1 2019195
2 200766
3 200854
4 200953
5 200951
6 200247
7 200444
8 201726
9 200419
10 200812
11 20127
12 20195
13 20005
14 20214
15 20203
16 20021
17 20240
18 20240

About Tae-Wook Ko

Tae-Wook Ko is a scholar working on Computer Networks and Communications, Statistical and Nonlinear Physics, Cognitive Neuroscience, Computational Theory and Mathematics and Atomic and Molecular Physics, and Optics, having authored 18 papers that have together received 592 indexed citations. Recurring topics across this work include Nonlinear Dynamics and Pattern Formation (14 papers), Neural dynamics and brain function (5 papers), Mechanical and Optical Resonators (4 papers), stochastic dynamics and bifurcation (4 papers), Machine Learning in Materials Science (3 papers), Complex Systems and Time Series Analysis (1 paper), Granular flow and fluidized beds (1 paper) and Nonlinear Photonic Systems (1 paper). The work is most often cited by research in Statistical and Nonlinear Physics (180 citations), Computer Networks and Communications (287 citations), Cognitive Neuroscience (206 citations), Materials Chemistry (176 citations) and Electrical and Electronic Engineering (143 citations). Tae-Wook Ko has collaborated with scholars based in South Korea, United States and France. Frequent co-authors include G. Bard Ermentrout, Hie‐Tae Moon, Hyunju Chang, YunKyong Hyon, Hyun Woo Kim, Jino Im, Sungwon Lee, Pan‐Jun Kim, Hawoong Jeong and Joseph J. Lee. Their work appears in journals such as Chaos Solitons & Fractals, Physica D Nonlinear Phenomena, Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences, Physical Review Letters and Chaos An Interdisciplinary Journal of Nonlinear Science.

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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