Andrew Glaws
Impact in
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- Model Reduction and Neural Networks
Papers in
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- Model Reduction and Neural Networks 10
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- Probabilistic and Robust Engineering Design 9
- Co-authors
- Ryan King (10 shared papers)Dylan Hettinger (1 shared paper)Michael Sprague (1 shared paper)Paul G. Constantine (4 shared papers)Dylan Harrison‐Atlas (4 shared papers)Grant Buster (2 shared papers)Ganesh Vijayakumar (3 shared papers)Shreyas Ananthan (1 shared paper)
- Journals
- Nature Energy (3 papers)Applied Energy (2 papers)Wind Energy (2 papers)AIAA Journal (2 papers)Journal of Computational Design and Engineering (1 paper)
- Partner nations
- United StatesEgyptGermany
In The Last Decade
Andrew Glaws
30 papers receiving 373 citations
Peers
Comparison fields: 5 of 67
- Computational Mathematics 4
- Statistical and Nonlinear Physics 62
- Atmospheric Science 88
- Statistics, Probability and Uncertainty 33
- Environmental Engineering 62
Countries citing papers authored by Andrew Glaws
This map shows the geographic impact of Andrew Glaws'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 Andrew Glaws with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Andrew Glaws more than expected).
Fields of papers citing papers by Andrew Glaws
This network shows the impact of papers produced by Andrew Glaws. 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 Andrew Glaws. The network helps show where Andrew Glaws may publish in the future.
Co-authors
The 25 scholars most cited alongside Andrew Glaws, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 31 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 144 | |
| 2 | 2020 | 37 | |
| 3 | 2024 | 24 | |
| 4 | 2022 | 19 | |
| 5 | 2020 | 17 | |
| 6 | 2024 | 16 | |
| 7 | 2021 | 15 | |
| 8 | 2023 | 14 | |
| 9 | 2021 | 13 | |
| 10 | 2017 | 11 | |
| 11 | 2019 | 11 | |
| 12 | 2016 | 10 | |
| 13 | 2024 | 9 | |
| 14 | 2020 | 8 | |
| 15 | 2023 | 6 | |
| 16 | 2022 | 4 | |
| 17 | 2022 | 3 | |
| 18 | 2020 | 3 | |
| 19 | 2025 | 2 | |
| 20 | 2025 | 2 |
About Andrew Glaws
Andrew Glaws is a scholar working on Statistical and Nonlinear Physics, Statistics, Probability and Uncertainty, Aerospace Engineering, Electrical and Electronic Engineering and Artificial Intelligence, having authored 31 papers that have together received 383 indexed citations. Recurring topics across this work include Model Reduction and Neural Networks (10 papers), Probabilistic and Robust Engineering Design (9 papers), Wind Energy Research and Development (7 papers), Wind and Air Flow Studies (4 papers), Advanced Multi-Objective Optimization Algorithms (4 papers), Computational Physics and Python Applications (3 papers), Meteorological Phenomena and Simulations (3 papers) and Energy Load and Power Forecasting (3 papers). The work is most often cited by research in Computational Mathematics (4 citations), Statistical and Nonlinear Physics (62 citations), Atmospheric Science (88 citations), Statistics, Probability and Uncertainty (33 citations) and Environmental Engineering (62 citations). Andrew Glaws has collaborated with scholars based in United States, Egypt and Germany. Frequent co-authors include Ryan King, Dylan Hettinger, Michael Sprague, Paul G. Constantine, Dylan Harrison‐Atlas, Grant Buster, Ganesh Vijayakumar, Shreyas Ananthan, Eric Lantz and R. Dennis Cook. Their work appears in journals such as Nature Energy, Applied Energy, Wind Energy, AIAA Journal and Journal of Computational Design and Engineering.
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.