Dragos B. Chirila
- Statistical and Nonlinear Physics top 10%
- Computational Mechanics
- Artificial Intelligence
- Atmospheric Science
- Global and Planetary Change
- Co-authors
- Adrian AlbertPrabhatHeng XiaoKarthik KashinathJinlong WuGerrit LohmannStefan HagemannPeter Krähé
- Topics
- Computational Physics and Python Applications (2 papers)Model Reduction and Neural Networks (1 paper)Climate Change Policy and Economics (1 paper)
- Journals
- Journal of Computational PhysicsHelmholtz-Zentrum für Polar-und Meeresforschung (Alfred-Wegener-Institut)Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B))
- Partner nations
- GermanyUnited States
In The Last Decade
Dragos B. Chirila
3 papers receiving 103 citations
Peers
Comparison fields: 5 of 48
- Statistical and Nonlinear Physics 52
- Computational Mechanics 29
- Artificial Intelligence 25
- Atmospheric Science 25
- Global and Planetary Change 19
Countries citing papers authored by Dragos B. Chirila
This map shows the geographic impact of Dragos B. Chirila'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 Dragos B. Chirila with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dragos B. Chirila more than expected).
Fields of papers citing papers by Dragos B. Chirila
This network shows the impact of papers produced by Dragos B. Chirila. 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 Dragos B. Chirila. The network helps show where Dragos B. Chirila may publish in the future.
Co-authorship network of co-authors of Dragos B. Chirila
This figure shows the co-authorship network connecting the top 25 collaborators of Dragos B. Chirila. A scholar is included among the top collaborators of Dragos B. Chirila 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 Dragos B. Chirila. Dragos B. Chirila is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 97 | |
| 2 | 3 | |
| 3 | Climate Model Bias Correction und die Deutsche Anpassungsstrategie | 13 |
About Dragos B. Chirila
Dragos B. Chirila is a scholar working on Statistical and Nonlinear Physics, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 3 papers that have together received 113 indexed citations. Recurring topics across this work include Computational Physics and Python Applications (2 papers), Model Reduction and Neural Networks (1 paper) and Climate Change Policy and Economics (1 paper). The work is most often cited by research in Statistical and Nonlinear Physics (52 citations), Computational Mechanics (29 citations) and Atmospheric Science (25 citations). Dragos B. Chirila has collaborated with scholars based in Germany and United States. Frequent co-authors include Adrian Albert, Prabhat, Heng Xiao, Karthik Kashinath, Jinlong Wu, Gerrit Lohmann, Stefan Hagemann, Peter Krähé, Jan O. Haerter and Christopher Moseley. Their work appears in journals such as Journal of Computational Physics, Helmholtz-Zentrum für Polar-und Meeresforschung (Alfred-Wegener-Institut) and Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)).
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.