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Andrew Glaws
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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
1602020
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
512020
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
242016
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
21*2020
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
202020
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
202017
Adversarial sampling of unknown and high-dimensional conditional distributions
M Hassanaly, A Glaws, K Stengel, RN King
Journal of Computational Physics 450, 110853, 2022
162022
Invertible neural networks for airfoil design
A Glaws, RN King, G Vijayakumar, S Ananthan
AIAA journal 60 (5), 3035-3047, 2022
152022
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
122022
Gauss–Christoffel quadrature for inverse regression: applications to computer experiments
A Glaws, PG Constantine
Statistics and Computing 29, 429-447, 2019
112019
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
102020
Gaussian quadrature and polynomial approximation for one-dimensional ridge functions
A Glaws, PG Constantine
SIAM Journal on Scientific Computing 41 (5), S106-S128, 2019
9*2019
Multi-fidelity active subspaces for wind farm uncertainty quantification
K Panda, R King, A Glaws, K Potter
AIAA Scitech 2021 Forum, 1601, 2021
72021
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
72020
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
62022
Multifidelity strategies for forward and inverse uncertainty quantification of wind energy applications
DT Seidl, G Geraci, R King, F Menhorn, A Glaws, MS Eldred
AIAA Scitech 2020 Forum, 1950, 2020
62020
Inverse regression for ridge recovery
AT Glaws, PG Constantine, RD Cook
2017 Graduate Research And Discovery Symposium (GRADS) posters and presentations, 2017
62017
Separable shape tensors for aerodynamic design
ZJ Grey, OA Doronina, A Glaws
Journal of Computational Design and Engineering 10 (1), 468-487, 2023
42023
Grassmannian shape representations for aerodynamic applications
OA Doronina, ZJ Grey, A Glaws
arXiv preprint arXiv:2201.04649, 2022
42022
Regularizing invertible neural networks for airfoil design through dimension reduction
A Glaws, J Hokanson, R King, G Vijayakumar
AIAA SCITECH 2022 Forum, 1098, 2022
42022
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