Julius Berner
Impact in
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- Model Reduction and Neural Networks
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- Neural Networks and Applications
- Anomaly Detection Techniques and Applications
- Machine Learning and Algorithms
Papers in
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- Model Reduction and Neural Networks 3
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- Artificial Intelligence in Healthcare and Education 1
- Co-authors
- Philipp Grohs (6 shared papers)Gitta Kutyniok (3 shared papers)Philipp Petersen (2 shared papers)Fabian Joachim Theis (1 shared paper)Felix Krahmer (1 shared paper)David Sebastian Fischer (1 shared paper)Arnulf Jentzen (2 shared papers)Simon Frieder (1 shared paper)
- Journals
- Frontiers in Public Health (1 paper)SIAM Journal on Mathematics of Data Science (1 paper)Cambridge University Press eBooks (2 papers)arXiv (Cornell University) (1 paper)Mitteilungen der Deutschen Mathematiker-Vereinigung (1 paper)
- Partner nations
- AustriaGermanySwitzerland
In The Last Decade
Julius Berner
6 papers receiving 178 citations
Peers
Comparison fields: 5 of 73
- Statistical and Nonlinear Physics 38
- Artificial Intelligence 76
- Infectious Diseases 29
- Computational Mathematics 1
- Numerical Analysis 8
Countries citing papers authored by Julius Berner
This map shows the geographic impact of Julius Berner'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 Julius Berner with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Julius Berner more than expected).
Fields of papers citing papers by Julius Berner
This network shows the impact of papers produced by Julius Berner. 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 Julius Berner. The network helps show where Julius Berner may publish in the future.
Co-authors
The 8 scholars most cited alongside Julius Berner, 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 | 76 | |
| 2 | 2022 | 55 | |
| 3 | 2021 | 36 | |
| 4 | 2020 | 6 | |
| 5 | 2019 | 5 | |
| 6 | 2023 | 4 | |
| 7 | 2021 | 0 |
About Julius Berner
Julius Berner is a scholar working on Statistical and Nonlinear Physics, Health Informatics, Computational Mechanics, Statistics, Probability and Uncertainty and Artificial Intelligence, having authored 7 papers that have together received 182 indexed citations. Recurring topics across this work include Model Reduction and Neural Networks (3 papers), Computational Physics and Python Applications (1 paper), Probabilistic and Robust Engineering Design (1 paper), Reservoir Engineering and Simulation Methods (1 paper), Artificial Intelligence in Healthcare and Education (1 paper), SARS-CoV-2 detection and testing (1 paper), Integrated Circuits and Semiconductor Failure Analysis (1 paper) and Advanced Numerical Methods in Computational Mathematics (1 paper). The work is most often cited by research in Statistical and Nonlinear Physics (38 citations), Artificial Intelligence (76 citations), Infectious Diseases (29 citations), Computational Mathematics (1 citation) and Numerical Analysis (8 citations). Julius Berner has collaborated with scholars based in Austria, Germany and Switzerland. Frequent co-authors include Philipp Grohs, Gitta Kutyniok, Philipp Petersen, Fabian Joachim Theis, Felix Krahmer, David Sebastian Fischer, Arnulf Jentzen and Simon Frieder. Their work appears in journals such as Frontiers in Public Health, SIAM Journal on Mathematics of Data Science, Cambridge University Press eBooks, arXiv (Cornell University) and Mitteilungen der Deutschen Mathematiker-Vereinigung.
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.