| Adversarial super-resolution of climatological wind and solar data K Stengel, A Glaws, D Hettinger, RN King Proceedings of the National Academy of Sciences 117 (29), 16805-16815, 2020 | 229 | 2020 |
| Deep learning for in situ data compression of large turbulent flow simulations A Glaws, R King, M Sprague Physical Review Fluids 5 (11), 114602, 2020 | 71 | 2020 |
| Unified architecture for data-driven metadata tagging of building automation systems S Mishra, A Glaws, D Cutler, S Frank, M Azam, F Mohammadi, JS Venne Automation in Construction 120, 103411, 2020 | 31* | 2020 |
| Invertible neural networks for airfoil design A Glaws, RN King, G Vijayakumar, S Ananthan AIAA journal 60 (5), 3035-3047, 2022 | 27 | 2022 |
| Python Active-subspaces Utility Library. PG Constantine, R Howard, AG Salinger, Z Grey, P Diaz, L Fletcher J. Open Source Softw. 1 (5), 79, 2016 | 27 | 2016 |
| Inverse regression for ridge recovery: a data-driven approach for parameter reduction in computer experiments A Glaws, PG Constantine, RD Cook Statistics and Computing 30, 237-253, 2020 | 24 | 2020 |
| Adversarial sampling of unknown and high-dimensional conditional distributions M Hassanaly, A Glaws, K Stengel, RN King Journal of Computational Physics 450, 110853, 2022 | 22 | 2022 |
| Dimension reduction in magnetohydrodynamics power generation models: Dimensional analysis and active subspaces A Glaws, PG Constantine, JN Shadid, TM Wildey Statistical Analysis and Data Mining: The ASA Data Science Journal 10 (5 …, 2017 | 21 | 2017 |
| High-resolution meteorology with climate change impacts from global climate model data using generative machine learning G Buster, BN Benton, A Glaws, RN King Nature Energy 9 (7), 894-906, 2024 | 17 | 2024 |
| Machine learning enables national assessment of wind plant controls with implications for land use D Harrison‐Atlas, RN King, A Glaws Wind Energy 25 (4), 618-638, 2022 | 17 | 2022 |
| A probabilistic approach to estimating wind farm annual energy production with bayesian quadrature R King, A Glaws, G Geraci, MS Eldred AIAA scitech 2020 forum, 1951, 2020 | 13 | 2020 |
| Artificial intelligence-aided wind plant optimization for nationwide evaluation of land use and economic benefits of wake steering D Harrison-Atlas, A Glaws, RN King, E Lantz Nature Energy 9 (6), 735-749, 2024 | 11 | 2024 |
| Gauss–Christoffel quadrature for inverse regression: applications to computer experiments A Glaws, PG Constantine Statistics and Computing 29, 429-447, 2019 | 11 | 2019 |
| Gaussian quadrature and polynomial approximation for one-dimensional ridge functions A Glaws, PG Constantine SIAM Journal on Scientific Computing 41 (5), S106-S128, 2019 | 11* | 2019 |
| Accelerated stress testing of perovskite photovoltaic modules: differentiating degradation modes with electroluminescence imaging JW Schall, A Glaws, NY Doumon, TJ Silverman, M Owen-Bellini, ... Solar RRL 7 (14), 2300229, 2023 | 9 | 2023 |
| Predictive analytics in future power systems: A panorama and state-of-the-art of deep learning applications S Mishra, A Glaws, P Palanisamy Optimization, learning, and control for interdependent complex networks, 147-182, 2020 | 9 | 2020 |
| Separable shape tensors for aerodynamic design ZJ Grey, OA Doronina, A Glaws Journal of Computational Design and Engineering 10 (1), 468-487, 2023 | 8 | 2023 |
| Wind turbine blade design with airfoil shape control using invertible neural networks J Jasa, A Glaws, P Bortolotti, G Vijayakumar, G Barter Journal of Physics: Conference Series 2265 (4), 042052, 2022 | 8 | 2022 |
| Multi-fidelity active subspaces for wind farm uncertainty quantification K Panda, R King, A Glaws, K Potter AIAA Scitech 2021 Forum, 1601, 2021 | 8 | 2021 |
| Physics-driven convolutional autoencoder approach for CFD data compressions A Olmo, A Zamzam, A Glaws, R King arXiv preprint arXiv:2210.09262, 2022 | 6 | 2022 |
