| 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 | 212 | 2020 |
| Control-oriented model for secondary effects of wake steering J King, P Fleming, R King, LA Martínez-Tossas, CJ Bay, R Mudafort, ... Wind Energy Science 6 (3), 701-714, 2021 | 130 | 2021 |
| Deep learning for presumed probability density function models MTH de Frahan, S Yellapantula, R King, MS Day, RW Grout Combustion and Flame 208, 436-450, 2019 | 71 | 2019 |
| Optimization under uncertainty for wake steering strategies J Quick, J Annoni, R King, K Dykes, P Fleming, A Ning Journal of physics: Conference series 854 (1), 012036, 2017 | 70 | 2017 |
| 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 | 64 | 2020 |
| Optimization of wind plant layouts using an adjoint approach RN King, K Dykes, P Graf, PE Hamlington Wind Energy Science 2 (1), 115-131, 2017 | 64 | 2017 |
| From deep to physics-informed learning of turbulence: Diagnostics R King, O Hennigh, A Mohan, M Chertkov arXiv preprint arXiv:1810.07785, 2018 | 61 | 2018 |
| Opportunities for research and development of hybrid power plants K Dykes, J King, N DiOrio, R King, V Gevorgian, D Corbus, N Blair, ... National Renewable Energy Lab.(NREL), Golden, CO (United States), 2020 | 58 | 2020 |
| Sensitivity analysis of wind plant performance to key turbine design parameters: a systems engineering approach K Dykes, A Ning, R King, P Graf, G Scott, PS Veers 32nd ASME Wind Energy Symposium, 1087, 2014 | 51 | 2014 |
| Wake steering optimization under uncertainty J Quick, J King, RN King, PE Hamlington, K Dykes Wind Energy Science 5 (1), 413-426, 2020 | 44 | 2020 |
| Autonomic closure for turbulence simulations RN King, PE Hamlington, WJA Dahm Physical Review E 93 (3), 031301, 2016 | 39 | 2016 |
| Bi-fidelity modeling of uncertain and partially unknown systems using deeponets S De, M Reynolds, M Hassanaly, RN King, A Doostan arXiv preprint arXiv:2204.00997, 2022 | 38 | 2022 |
| Cardiopulmonary fitness is strongly associated with body cell mass and fat‐free mass: The Study of Health in Pomerania (SHIP) A Köhler, R King, M Bahls, S Groß, A Steveling, S Gärtner, S Schipf, ... Scandinavian journal of medicine & science in sports 28 (6), 1628-1635, 2018 | 33 | 2018 |
| A systems engineering analysis of three‐point and four‐point wind turbine drivetrain configurations Y Guo, T Parsons, K Dykes, RN King Wind Energy 20 (3), 537-550, 2017 | 30 | 2017 |
| Effect of tip-speed constraints on the optimized design of a wind turbine K Dykes, B Resor, A Platt, Y Guo, A Ning, R King, T Parsons, D Petch, ... National Renewable Energy Lab.(NREL), Golden, CO (United States), 2014 | 30 | 2014 |
| Analytical Formulation for Sizing and Estimating the Dimensions and Weight of Wind Turbine Hub and Drivetrain Components Y Guo, T Parsons, R King, K Dykes, P Veers National Renewable Energy Lab.(NREL), Golden, CO (United States), 2015 | 27 | 2015 |
| A systematic approach to offshore wind turbine jacket predesign and optimization: geometry, cost, and surrogate structural code check models J Häfele, RR Damiani, RN King, CG Gebhardt, R Rolfes Wind Energy Science 3 (2), 553-572, 2018 | 23 | 2018 |
| Invertible neural networks for airfoil design A Glaws, RN King, G Vijayakumar, S Ananthan AIAA journal 60 (5), 3035-3047, 2022 | 21 | 2022 |
| Adversarial sampling of unknown and high-dimensional conditional distributions M Hassanaly, A Glaws, K Stengel, RN King Journal of Computational Physics 450, 110853, 2022 | 21 | 2022 |
| Data-driven wind farm optimization incorporating effects of turbulence intensity C Adcock, RN King 2018 Annual American Control Conference (ACC), 695-700, 2018 | 18 | 2018 |
