Andrew Glaws

716 total citations
31 papers, 383 citations indexed

About

Andrew Glaws is a scholar working on Statistical and Nonlinear Physics, Statistics, Probability and Uncertainty and Aerospace Engineering. According to data from OpenAlex, Andrew Glaws has authored 31 papers receiving a total of 383 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Statistical and Nonlinear Physics, 9 papers in Statistics, Probability and Uncertainty and 8 papers in Aerospace Engineering. Recurrent topics in Andrew Glaws's work include Model Reduction and Neural Networks (10 papers), Probabilistic and Robust Engineering Design (9 papers) and Wind Energy Research and Development (7 papers). Andrew Glaws is often cited by papers focused on Model Reduction and Neural Networks (10 papers), Probabilistic and Robust Engineering Design (9 papers) and Wind Energy Research and Development (7 papers). Andrew Glaws collaborates with scholars based in United States, Germany and Egypt. Andrew Glaws's co-authors include Ryan King, Dylan Hettinger, Michael Sprague, Paul G. Constantine, Dylan Harrison‐Atlas, Grant Buster, Ganesh Vijayakumar, Shreyas Ananthan, Malik Hassanaly and Eric Lantz and has published in prestigious journals such as Proceedings of the National Academy of Sciences, SHILAP Revista de lepidopterología and Applied Energy.

In The Last Decade

Andrew Glaws

30 papers receiving 373 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Andrew Glaws United States 11 88 75 73 67 63 31 383
Sang-Seung Lee South Korea 16 92 1.0× 157 2.1× 57 0.8× 114 1.7× 31 0.5× 82 779
César Quilodrán-Casas United Kingdom 5 57 0.6× 24 0.3× 49 0.7× 35 0.5× 15 0.2× 8 249
Claire E. Heaney United Kingdom 12 81 0.9× 172 2.3× 33 0.5× 96 1.4× 15 0.2× 37 555
Sergey Voronin Ukraine 10 84 1.0× 75 1.0× 92 1.3× 16 0.2× 75 1.2× 54 541
Meng Tang United States 8 23 0.3× 61 0.8× 32 0.4× 41 0.6× 47 0.7× 13 626
J. Chandrasekar United States 11 54 0.6× 72 1.0× 37 0.5× 170 2.5× 22 0.3× 29 573
Dominic Masters United Kingdom 10 42 0.5× 235 3.1× 66 0.9× 93 1.4× 12 0.2× 11 408
Zhihui Lin China 9 79 0.9× 12 0.2× 68 0.9× 60 0.9× 106 1.7× 22 457
Lianlei Lin China 10 52 0.6× 133 1.8× 18 0.2× 29 0.4× 36 0.6× 40 337

Countries citing papers authored by Andrew Glaws

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Andrew Glaws

This figure shows the co-authorship network connecting the top 25 collaborators of Andrew Glaws. A scholar is included among the top collaborators of Andrew Glaws based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Andrew Glaws. Andrew Glaws is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Glaws, Andrew, et al.. (2025). Explainable artificial intelligence relates perovskite luminescence images to current-voltage metrics. SHILAP Revista de lepidopterología. 22. 100640–100640.
2.
Chen, Xin, Baojie Li, Jennifer L. Braid, et al.. (2025). Open data sets for assessing photovoltaic system reliability. Applied Energy. 395. 126132–126132. 2 indexed citations
3.
Glaws, Andrew, Hilary Egan, Brian C. Wyatt, et al.. (2025). Mind the gap: Bridging the divide between AI aspirations and the reality of autonomous microscopy. ArXiv.org. 3(2). 2 indexed citations
4.
Glaws, Andrew, Grant Buster, Xiangyu Zhang, et al.. (2025). Designing Future Energy Systems With Generative AI. Computing in Science & Engineering. 27(4). 8–18. 2 indexed citations
5.
Glaws, Andrew, et al.. (2025). Aerodynamic Sensitivities over Separable Shape Tensors. AIAA Journal. 63(7). 2707–2720. 2 indexed citations
6.
Zhang, Xiangyu, et al.. (2024). Deep generative models in energy system applications: Review, challenges, and future directions. Applied Energy. 380. 125059–125059. 9 indexed citations
7.
Sethuraman, Latha, et al.. (2024). Advanced multimaterial shape optimization methods as applied to advanced manufacturing of wind turbine generators. Wind Energy. 27(8). 767–796. 2 indexed citations
8.
Glaws, Andrew, Nutifafa Y. Doumon, Timothy J. Silverman, et al.. (2023). Accelerated Stress Testing of Perovskite Photovoltaic Modules: Differentiating Degradation Modes with Electroluminescence Imaging. Solar RRL. 7(14). 14 indexed citations
9.
Glaws, Andrew, et al.. (2023). Separable shape tensors for aerodynamic design. Journal of Computational Design and Engineering. 10(1). 468–487. 6 indexed citations
10.
Glaws, Andrew, et al.. (2023). Automated Design-for-Reliability of a Power Electronics Module. 1. 1–6. 1 indexed citations
11.
Sethuraman, Latha, et al.. (2023). Advanced permanent magnet generator topologies using multimaterial shape optimization and 3D printing. IET conference proceedings.. 2023(17). 478–485. 1 indexed citations
12.
Hassanaly, Malik, et al.. (2021). Adversarial sampling of unknown and high-dimensional conditional distributions. arXiv (Cornell University). 15 indexed citations
13.
Harrison‐Atlas, Dylan, Ryan King, & Andrew Glaws. (2021). Machine learning enables national assessment of wind plant controls with implications for land use. Wind Energy. 25(4). 618–638. 13 indexed citations
14.
King, Ryan, et al.. (2021). Multi-fidelity Active Subspaces for Wind Farm Uncertainty Quantification. AIAA Scitech 2021 Forum. 2 indexed citations
15.
Glaws, Andrew, Ryan King, & Michael Sprague. (2020). Deep learning forin situdata compression of large turbulent flow simulations. Physical Review Fluids. 5(11). 37 indexed citations
16.
Glaws, Andrew, et al.. (2019). Physics-Informed Super Resolution of Climatological Wind and Solar Resource Data. AGU Fall Meeting Abstracts. 2019. 1 indexed citations
17.
Glaws, Andrew, Paul G. Constantine, & R. Dennis Cook. (2019). Inverse regression for ridge recovery: a data-driven approach for parameter reduction in computer experiments. Statistics and Computing. 30(2). 237–253. 11 indexed citations
18.
Glaws, Andrew & Paul G. Constantine. (2019). Gaussian Quadrature and Polynomial Approximation for One-Dimensional Ridge Functions. SIAM Journal on Scientific Computing. 41(5). S106–S128. 2 indexed citations
19.
Glaws, Andrew, Paul G. Constantine, John N. Shadid, & Timothy Wildey. (2017). Dimension reduction in magnetohydrodynamics power generation models: Dimensional analysis and active subspaces. Statistical Analysis and Data Mining The ASA Data Science Journal. 10(5). 312–325. 11 indexed citations
20.
Constantine, Paul G., et al.. (2016). Python Active-subspaces Utility Library. The Journal of Open Source Software. 1(5). 79–79. 10 indexed citations

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.

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