Alexander Kuhnle
-
- Model Reduction and Neural Networks 2
- Computational Mechanics top 10%
- Fluid Dynamics and Turbulent Flows 1
-
- Reinforcement Learning in Robotics 4
- Topic Modeling 3
- Natural Language Processing Techniques 3
- Artificial Intelligence in Games 2
-
- semigroups and automata theory 1
-
- Optimization and Search Problems 1
- Co-authors
- Jean RabaultJonathan ViqueratAurélien LarcherElie HachemAndrew D. BagdanovGuy EmersonAnn CopestakeHaoyue Zhu
- Journals
- Journal of Computational Physics (1 paper)Computers & Fluids (1 paper)Language Resources and Evaluation (1 paper)
- Partner nations
- United KingdomNorwayItaly
In The Last Decade
Alexander Kuhnle
8 papers receiving 214 citations
Hit Papers
Peers
Comparison fields: 5 of 51
- Statistical and Nonlinear Physics 99
- Computational Mechanics 114
- Aerospace Engineering 52
- Artificial Intelligence 59
- Ocean Engineering 20
Countries citing papers authored by Alexander Kuhnle
This map shows the geographic impact of Alexander Kuhnle'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 Alexander Kuhnle with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alexander Kuhnle more than expected).
Fields of papers citing papers by Alexander Kuhnle
This network shows the impact of papers produced by Alexander Kuhnle. 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 Alexander Kuhnle. The network helps show where Alexander Kuhnle may publish in the future.
Co-authorship network
The 13 scholars most cited alongside Alexander Kuhnle, 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 | 2021 | 3 | |
| 2 | 2021 | 7 | |
| 3 | 2021 | 5 | |
| 4 | A review on deep reinforcement learning for fluid mechanicsbreakdown → | 2021 | 186 |
| 5 | 2020 | 6 | |
| 6 | Accelerating Deep Reinforcement Learning of Active Flow Control strategies through a multi-environment approach | 2019 | 3 |
| 7 | Resources for building applications with Dependency Minimal Recursion Semantics | 2016 | 10 |
| 8 | 2016 | 8 | |
| 9 | Modeling Uncertain Data using Monads and an Application to the Sequence Alignment Problem | 2013 | 1 |
About Alexander Kuhnle
Alexander Kuhnle is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics and Computational Mechanics, having authored 9 papers that have together received 229 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (4 papers), Topic Modeling (3 papers), Natural Language Processing Techniques (3 papers), Model Reduction and Neural Networks (2 papers), Artificial Intelligence in Games (2 papers), Fluid Dynamics and Turbulent Flows (1 paper), semigroups and automata theory (1 paper) and Optimization and Search Problems (1 paper). The work is most often cited by research in Statistical and Nonlinear Physics (99 citations), Computational Mechanics (114 citations) and Aerospace Engineering (52 citations). Alexander Kuhnle has collaborated with scholars based in United Kingdom, Norway and Italy. Frequent co-authors include Jean Rabault, Jonathan Viquerat, Aurélien Larcher, Elie Hachem, Andrew D. Bagdanov, Guy Emerson, Ann Copestake, Haoyue Zhu, Michael Wayne Goodman and Simone Teufel. Their work appears in journals such as Journal of Computational Physics, Computers & Fluids and Language Resources and Evaluation.
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.