David Flad
Impact in
- Computational Mechanics top 2%
- Fluid Dynamics and Turbulent Flows
- Computational Fluid Dynamics and Aerodynamics
- Advanced Numerical Methods in Computational Mathematics
- Fluid Dynamics and Vibration Analysis
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- Model Reduction and Neural Networks
Papers in
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- Fluid Dynamics and Turbulent Flows 6
- Advanced Numerical Methods in Computational Mathematics 3
- Lattice Boltzmann Simulation Studies 3
- Fluid Dynamics and Vibration Analysis 3
- Computational Fluid Dynamics and Aerodynamics 2
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- Model Reduction and Neural Networks 6
- Co-authors
- Andrea Beck (9 shared papers)Claus‐Dieter Munz (7 shared papers)Gregor J. Gassner (4 shared papers)Florian Hindenlang (1 shared paper)Daniel J. Garmann (1 shared paper)A V Garbaruk (1 shared paper)Iván Bermejo-Moreno (1 shared paper)Johan Larsson (1 shared paper)
- Journals
- Journal of Computational Physics (5 papers)Flow Turbulence and Combustion (2 papers)International Journal for Numerical Methods in Fluids (1 paper)arXiv (Cornell University) (2 papers)
- Partner nations
- GermanyUnited StatesRussia
In The Last Decade
David Flad
12 papers receiving 588 citations
David Flad's Hit Papers
Peers
Comparison fields: 5 of 35
- Computational Mechanics 548
- Statistical and Nonlinear Physics 212
- Environmental Engineering 84
- Atmospheric Science 83
- Aerospace Engineering 107
Countries citing papers authored by David Flad
This map shows the geographic impact of David Flad'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 David Flad with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Flad more than expected).
Fields of papers citing papers by David Flad
This network shows the impact of papers produced by David Flad. 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 David Flad. The network helps show where David Flad may publish in the future.
Co-authors
The 12 scholars most cited alongside David Flad, 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 | Deep neural networks for data-driven LES closure models Hit paper breakdown → | 2019 | 216 |
| 2 | 2014 | 163 | |
| 3 | 2017 | 66 | |
| 4 | 2015 | 53 | |
| 5 | 2020 | 41 | |
| 6 | 2016 | 27 | |
| 7 | 2018 | 16 | |
| 8 | 2014 | 15 | |
| 9 | 2020 | 7 | |
| 10 | Neural Networks for Data-Based Turbulence Models | 2018 | 3 |
| 11 | 2024 | 2 | |
| 12 | 2023 | 1 |
About David Flad
David Flad is a scholar working on Computational Mechanics, Statistical and Nonlinear Physics, Atmospheric Science, Environmental Engineering and Aerospace Engineering, having authored 12 papers that have together received 610 indexed citations. Recurring topics across this work include Model Reduction and Neural Networks (6 papers), Fluid Dynamics and Turbulent Flows (6 papers), Advanced Numerical Methods in Computational Mathematics (3 papers), Lattice Boltzmann Simulation Studies (3 papers), Fluid Dynamics and Vibration Analysis (3 papers), Wind and Air Flow Studies (2 papers), Meteorological Phenomena and Simulations (2 papers) and Computational Fluid Dynamics and Aerodynamics (2 papers). The work is most often cited by research in Computational Mechanics (548 citations), Statistical and Nonlinear Physics (212 citations), Environmental Engineering (84 citations), Atmospheric Science (83 citations) and Aerospace Engineering (107 citations). David Flad has collaborated with scholars based in Germany, United States and Russia. Frequent co-authors include Andrea Beck, Claus‐Dieter Munz, Gregor J. Gassner, Florian Hindenlang, Daniel J. Garmann, A V Garbaruk, Iván Bermejo-Moreno, Johan Larsson, Florian Menter and Christoph Brehm. Their work appears in journals such as Journal of Computational Physics, Flow Turbulence and Combustion, International Journal for Numerical Methods in Fluids and arXiv (Cornell University).
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.