Bogdan Georgiev
- Artificial Intelligence top 10%
- Neural Networks and Applications 3
- Quantum Computing Algorithms and Architecture 2
- Quantum Information and Cryptography 2
- Machine Learning and Algorithms 1
- Neural Networks and Reservoir Computing 1
- Control and Systems Engineering top 10%
- Stability and Controllability of Differential Equations 1
-
- Advanced Mathematical Modeling in Engineering 2
-
- Nonlinear Differential Equations Analysis 1
- Co-authors
- Raoul HeeseChristian BauckhageSven GiesselbachSebastian MayerBirgit KirschKatharina BeckhRajkumar RamamurthyJannis Schuecker
- Journals
- Communications in Mathematical Physics (1 paper)IEEE Transactions on Knowledge and Data Engineering (1 paper)Potential Analysis (1 paper)
- Partner nations
- GermanyUnited StatesIndia
In The Last Decade
Bogdan Georgiev
6 papers receiving 460 citations
Hit Papers
Peers
Comparison fields: 5 of 104
- Health Informatics 10
- Artificial Intelligence 186
- Statistical and Nonlinear Physics 46
- Control and Systems Engineering 69
- Industrial and Manufacturing Engineering 28
Countries citing papers authored by Bogdan Georgiev
This map shows the geographic impact of Bogdan Georgiev'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 Bogdan Georgiev with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bogdan Georgiev more than expected).
Fields of papers citing papers by Bogdan Georgiev
This network shows the impact of papers produced by Bogdan Georgiev. 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 Bogdan Georgiev. The network helps show where Bogdan Georgiev may publish in the future.
Co-authorship network
The 14 scholars most cited alongside Bogdan Georgiev, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2022 | 8 | |
| 2 | 2021 | 1 | |
| 3 | Informed Machine Learning - A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systemsbreakdown → | 2021 | 469 |
| 4 | Explorations in Quantum Neural Networks with Intermediate Measurements. | 2020 | 1 |
| 5 | 2020 | 1 | |
| 6 | 2017 | 1 | |
| 7 | 2013 | 1 | |
| 8 | 2013 | 1 |
About Bogdan Georgiev
Bogdan Georgiev is a scholar working on Mathematical Physics, Statistical and Nonlinear Physics and Computational Theory and Mathematics, having authored 8 papers that have together received 483 indexed citations. Recurring topics across this work include Neural Networks and Applications (3 papers), Quantum Computing Algorithms and Architecture (2 papers), Quantum Information and Cryptography (2 papers), Advanced Mathematical Modeling in Engineering (2 papers), Nonlinear Differential Equations Analysis (1 paper), Machine Learning and Algorithms (1 paper), Stability and Controllability of Differential Equations (1 paper) and Neural Networks and Reservoir Computing (1 paper). The work is most often cited by research in Health Informatics (10 citations), Artificial Intelligence (186 citations) and Statistical and Nonlinear Physics (46 citations). Bogdan Georgiev has collaborated with scholars based in Germany, United States and India. Frequent co-authors include Raoul Heese, Christian Bauckhage, Sven Giesselbach, Sebastian Mayer, Birgit Kirsch, Katharina Beckh, Rajkumar Ramamurthy, Jannis Schuecker, Michał Walczak and Laura von Rueden. Their work appears in journals such as Communications in Mathematical Physics, IEEE Transactions on Knowledge and Data Engineering and Potential Analysis.
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.