Takahiro Miyoshi
- Astronomy and Astrophysics top 5%
- Computational Mechanics top 5%
- Nuclear and High Energy Physics top 10%
- Artificial Intelligence top 10%
- Applied Mathematics top 5%
- Co-authors
- K. KusanoH. IchihashiKazunori NagasakaSeiji ZenitaniAtsuo MurataMasashi ChibaMunehisa SekikawaNaohiko Inaba
- Topics
- Neural Networks and Applications (17 papers)Ionosphere and magnetosphere dynamics (16 papers)Solar and Space Plasma Dynamics (15 papers)
- Journals
- Journal of Geophysical Research AtmospheresThe Astrophysical JournalJournal of Computational Physics
- Partner nations
- JapanUnited StatesUnited Kingdom
In The Last Decade
Takahiro Miyoshi
60 papers receiving 970 citations
Hit Papers
Peers
Comparison fields: 5 of 94
- Astronomy and Astrophysics 565
- Computational Mechanics 206
- Nuclear and High Energy Physics 171
- Artificial Intelligence 119
- Applied Mathematics 105
Countries citing papers authored by Takahiro Miyoshi
This map shows the geographic impact of Takahiro Miyoshi'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 Takahiro Miyoshi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Takahiro Miyoshi more than expected).
Fields of papers citing papers by Takahiro Miyoshi
This network shows the impact of papers produced by Takahiro Miyoshi. 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 Takahiro Miyoshi. The network helps show where Takahiro Miyoshi may publish in the future.
Co-authorship network of co-authors of Takahiro Miyoshi
This figure shows the co-authorship network connecting the top 25 collaborators of Takahiro Miyoshi. A scholar is included among the top collaborators of Takahiro Miyoshi 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 Takahiro Miyoshi. Takahiro Miyoshi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 0 | |
| 4 | 2 | |
| 5 | 0 | |
| 6 | 6 | |
| 7 | 8 | |
| 8 | 4 | |
| 9 | 6 | |
| 10 | 10 | |
| 11 | 11 | |
| 12 | 4 | |
| 13 | 20 | |
| 14 | Simulation study of magnetic reconnection in high magnetic Reynolds number plasmas | 1 |
| 15 | 25 | |
| 16 | Robust and Efficient Riemann Solvers for MHD | 3 |
| 17 | An MHD Simulation of the Magnetosphere Based on the HLLD Approximate Riemann Solver | 1 |
| 18 | 8 | |
| 19 | 3 | |
| 20 | 3 |
About Takahiro Miyoshi
Takahiro Miyoshi is a scholar working on Astronomy and Astrophysics, Nuclear and High Energy Physics and Artificial Intelligence, having authored 71 papers that have together received 1.0k indexed citations. Recurring topics across this work include Neural Networks and Applications (17 papers), Ionosphere and magnetosphere dynamics (16 papers) and Solar and Space Plasma Dynamics (15 papers). The work is most often cited by research in Astronomy and Astrophysics (565 citations), Nuclear and High Energy Physics (171 citations) and Applied Mathematics (105 citations). Takahiro Miyoshi has collaborated with scholars based in Japan, United States and United Kingdom. Frequent co-authors include K. Kusano, H. Ichihashi, Kazunori Nagasaka, Seiji Zenitani, Atsuo Murata, Masashi Chiba, Munehisa Sekikawa, Naohiko Inaba, Hiroyuki R. Takahashi and Chiho Nonaka. Their work appears in journals such as Journal of Geophysical Research Atmospheres, The Astrophysical Journal and Journal of Computational Physics.
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.