Andrew Glaws
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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 (2), 237-253, 2020
Python active-subspaces utility library
P Constantine, R Howard, A Glaws, Z Grey, P Diaz, L Fletcher
Journal of Open Source Software 1 (5), 79, 2016
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
Gauss–Christoffel quadrature for inverse regression: applications to computer experiments
A Glaws, PG Constantine
Statistics and Computing 29 (3), 429-447, 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
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
Inverse regression for ridge recovery: A data-driven approach for parameter space dimension reduction in computational science
A Glaws, PG Constantine, RD Cook
arXiv preprint arXiv:1702.02227, 2017
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
Finite element simulations of two dimensional peridynamic models
AT Glaws
Virginia Tech, 2014
Data-Driven Metadata Tagging for Building Automation Systems: A Unified Architecture
S Mishra, A Glaws, D Cutler, S Frank, M Azam, F Mohammadi, JS Venne
arXiv preprint arXiv:2003.07690, 2020
Physics-Informed Super Resolution of Climatological Wind and Solar Resource Data
K Stengel, A Glaws, R King
AGUFM 2019, A43E-04, 2019
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
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
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
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
Deep Learning for In-situ Compression of Large CFD Simulations
R King, A Glaws, M Sprague
APS, P17. 008, 2019
Parameter Dimension Reduction for Scientific Computing
AT Glaws
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