Eunsu Park

592 total citations
26 papers, 424 citations indexed

About

Eunsu Park is a scholar working on Astronomy and Astrophysics, Artificial Intelligence and Molecular Biology. According to data from OpenAlex, Eunsu Park has authored 26 papers receiving a total of 424 indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Astronomy and Astrophysics, 18 papers in Artificial Intelligence and 3 papers in Molecular Biology. Recurrent topics in Eunsu Park's work include Solar and Space Plasma Dynamics (22 papers), Solar Radiation and Photovoltaics (18 papers) and Stellar, planetary, and galactic studies (6 papers). Eunsu Park is often cited by papers focused on Solar and Space Plasma Dynamics (22 papers), Solar Radiation and Photovoltaics (18 papers) and Stellar, planetary, and galactic studies (6 papers). Eunsu Park collaborates with scholars based in South Korea, Belgium and Australia. Eunsu Park's co-authors include Yong‐Jae Moon, Harim Lee, Daye Lim, Gyungin Shin, Sujin Lee, Tae Young Kim, Il‐Hyun Cho, Jin‐Yi Lee, Myungjin Choi and Kyung‐Suk Cho and has published in prestigious journals such as The Astrophysical Journal, The Astrophysical Journal Supplement Series and Astronomy and Astrophysics.

In The Last Decade

Eunsu Park

22 papers receiving 370 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Eunsu Park South Korea 11 339 193 71 71 58 26 424
Il‐Hyun Cho South Korea 13 520 1.5× 91 0.5× 23 0.3× 130 1.8× 44 0.8× 34 584
Daye Lim South Korea 10 306 0.9× 164 0.8× 56 0.8× 48 0.7× 11 0.2× 28 356
Bingxian Luo China 14 483 1.4× 92 0.5× 18 0.3× 156 2.2× 114 2.0× 59 537
Yongyuan Xiang China 15 614 1.8× 85 0.4× 31 0.4× 108 1.5× 10 0.2× 33 662
Martin Reiß Austria 13 428 1.3× 73 0.4× 25 0.4× 152 2.1× 11 0.2× 29 543
S. Zharkov United Kingdom 18 966 2.8× 224 1.2× 47 0.7× 171 2.4× 34 0.6× 63 1.0k
Linhua Deng China 16 588 1.7× 156 0.8× 19 0.3× 147 2.1× 16 0.3× 78 659
Yuanyong Deng China 18 1.2k 3.6× 124 0.6× 18 0.3× 317 4.5× 22 0.4× 125 1.3k
Tatiana Podladchikova Russia 13 422 1.2× 97 0.5× 18 0.3× 89 1.3× 20 0.3× 50 483
Su‐Chan Bong South Korea 19 826 2.4× 100 0.5× 13 0.2× 201 2.8× 22 0.4× 57 858

Countries citing papers authored by Eunsu Park

Since Specialization
Citations

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

Fields of papers citing papers by Eunsu Park

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Eunsu Park

This figure shows the co-authorship network connecting the top 25 collaborators of Eunsu Park. A scholar is included among the top collaborators of Eunsu Park 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 Eunsu Park. Eunsu Park 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.
Park, Eunsu, et al.. (2025). Artificial-intelligence-based Reconstruction of Solar Farside Vector Magnetograms from Multispacecraft Extreme-ultraviolet Data. The Astrophysical Journal Supplement Series. 281(2). 63–63.
2.
Youn, Jae‐Hyuck, et al.. (2025). Can we properly determine differential emission measures from Solar Orbiter/EUI/FSI with deep learning?. Astronomy and Astrophysics. 695. A125–A125. 1 indexed citations
3.
Park, Eunsu, Harim Lee, Yong‐Jae Moon, et al.. (2023). Pixel-to-pixel Translation of Solar Extreme-ultraviolet Images for DEMs by Fully Connected Networks. The Astrophysical Journal Supplement Series. 264(2). 33–33. 2 indexed citations
4.
Moon, Yong‐Jae, et al.. (2023). Fast Reconstruction of 3D Density Distribution around the Sun Based on the MAS by Deep Learning. The Astrophysical Journal. 948(1). 21–21. 5 indexed citations
5.
Moon, Yong‐Jae, et al.. (2022). Improved AI-generated Solar Farside Magnetograms by STEREO and SDO Data Sets and Their Release. The Astrophysical Journal Supplement Series. 262(2). 50–50. 20 indexed citations
6.
Lee, Harim, Eunsu Park, Il‐Hyun Cho, et al.. (2022). Generation of Solar Coronal White-light Images from SDO/AIA EUV Images by Deep Learning. The Astrophysical Journal. 937(2). 111–111. 4 indexed citations
7.
Chae, Jongchul, et al.. (2022). Deep Learning–based Fast Spectral Inversion of Hα and Ca ii 8542 Line Spectra. The Astrophysical Journal. 940(2). 147–147. 5 indexed citations
8.
Moon, Yong‐Jae, et al.. (2021). Visual Explanation of a Deep Learning Solar Flare Forecast Model and Its Relationship to Physical Parameters. The Astrophysical Journal. 910(1). 8–8. 24 indexed citations
9.
Lim, Daye, Yong‐Jae Moon, Eunsu Park, & Jin‐Yi Lee. (2021). Selection of Three (Extreme)Ultraviolet Channels for Solar Satellite Missions by Deep Learning. The Astrophysical Journal Letters. 915(2). L31–L31. 8 indexed citations
10.
Lee, Harim, Eunsu Park, & Yong‐Jae Moon. (2021). Generation of Modern Satellite Data from Galileo Sunspot Drawings in 1612 by Deep Learning. The Astrophysical Journal. 907(2). 118–118. 8 indexed citations
11.
Park, Eunsu, et al.. (2021). Reply to: Reliability of AI-generated magnetograms from only EUV images. Nature Astronomy. 5(2). 111–112. 3 indexed citations
12.
Park, Eunsu, Yong‐Jae Moon, Daye Lim, & Harim Lee. (2020). De-noising SDO/HMI Solar Magnetograms by Image Translation Method Based on Deep Learning. The Astrophysical Journal Letters. 891(1). L4–L4. 12 indexed citations
13.
Lee, Sujin, et al.. (2020). One‐Day Forecasting of Global TEC Using a Novel Deep Learning Model. Space Weather. 19(1). 48 indexed citations
14.
Moon, Yong‐Jae, et al.. (2020). Improvement of IRI Global TEC Maps by Deep Learning Based on Conditional Generative Adversarial Networks. Space Weather. 18(5). 24 indexed citations
15.
Shin, Gyungin, et al.. (2020). Generation of High-resolution Solar Pseudo-magnetograms from Ca ii K Images by Deep Learning. The Astrophysical Journal Letters. 895(1). L16–L16. 25 indexed citations
16.
Moon, Yong‐Jae, et al.. (2020). Super-resolution of SDO/HMI Magnetograms Using Novel Deep Learning Methods. The Astrophysical Journal Letters. 897(2). L32–L32. 20 indexed citations
17.
Kim, Tae Young, Eunsu Park, Harim Lee, et al.. (2019). Solar farside magnetograms from deep learning analysis of STEREO/EUVI data. Nature Astronomy. 3(5). 397–400. 75 indexed citations
18.
Park, Eunsu, Yong‐Jae Moon, Jin‐Yi Lee, et al.. (2019). Generation of Solar UV and EUV Images from SDO/HMI Magnetograms by Deep Learning. The Astrophysical Journal Letters. 884(1). L23–L23. 33 indexed citations
19.
Lim, Daye, Yong‐Jae Moon, Eunsu Park, et al.. (2019). Ensemble Forecasting of Major Solar Flares with Short-, Mid-, and Long-term Active Region Properties. The Astrophysical Journal. 885(1). 35–35. 8 indexed citations
20.
Park, Eunsu, et al.. (2018). Application of the Deep Convolutional Neural Network to the Forecast of Solar Flare Occurrence Using Full-disk Solar Magnetograms. The Astrophysical Journal. 869(2). 91–91. 68 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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