| Machine learning for nonintrusive model order reduction of the parametric inviscid transonic flow past an airfoil SA Renganathan, R Maulik, V Rao Physics of Fluids 32 (4), 047110, 2020 | 54 | 2020 |
| Enhanced data efficiency using deep neural networks and Gaussian processes for aerodynamic design optimization SA Renganathan, R Maulik, J Ahuja Aerospace Science and Technology 111, 106522, 2021 | 35 | 2021 |
| Empirical assessment of deep gaussian process surrogate models for engineering problems D Rajaram, TG Puranik, S Ashwin Renganathan, WJ Sung, OP Fischer, ... Journal of Aircraft 58 (1), 182-196, 2021 | 19 | 2021 |
| Deep Gaussian process enabled surrogate models for aerodynamic flows D Rajaram, TG Puranik, A Renganathan, WJ Sung, OJ Pinon-Fischer, ... AIAA Scitech 2020 Forum, 1640, 2020 | 19 | 2020 |
| Distributed hierarchical control system for a tandem axle drive system RA Nellums, A Surianarayanan, SA Joshi, SC Krishnan, DG Smedley, ... US Patent 9,020,715, 2015 | 17 | 2015 |
| Aerodynamic data fusion toward the digital twin paradigm SA Renganathan, K Harada, DN Mavris AIAA Journal 58 (9), 3902-3918, 2020 | 16 | 2020 |
| Koopman-based approach to nonintrusive projection-based reduced-order modeling with black-box high-fidelity models SA Renganathan, Y Liu, DN Mavris AIAA Journal 56 (10), 4087-4111, 2018 | 16 | 2018 |
| Numerical analysis of fuel—air mixing in a two-dimensional trapped vortex combustor DP Mishra, R Sudharshan Proceedings of the Institution of Mechanical Engineers, Part G: Journal of …, 2010 | 16 | 2010 |
| Sensitivity analysis of aero-propulsive coupling for over-wing-nacelle concepts A Renganathan, SH Berguin, M Chen, J Ahuja, JC Tai, DN Mavris, D Hills 2018 AIAA Aerospace Sciences Meeting, 1757, 2018 | 12 | 2018 |
| Data-driven wind turbine wake modeling via probabilistic machine learning S Ashwin Renganathan, R Maulik, S Letizia, GV Iungo Neural Computing and Applications, 1-16, 2022 | 11 | 2022 |
| Koopman-based approach to nonintrusive reduced order modeling: Application to aerodynamic shape optimization and uncertainty propagation SA Renganathan AIAA Journal 58 (5), 2221-2235, 2020 | 11 | 2020 |
| A Methodology for Non-Intrusive projection-based model reduction of expensive black-box PDE-based systems and application in the many-query context SA Renganathan Georgia Institute of Technology, 2018 | 8 | 2018 |
| Numerical study of flame/vortex interactions in 2-D Trapped Vortex Combustor PD Mishra, R Sudharshan, KKP Ezhil Thermal Science 18 (4), 1373-1387, 2014 | 6 | 2014 |
| Lookahead acquisition functions for finite-horizon time-dependent bayesian optimization and application to quantum optimal control SA Renganathan, J Larson, SM Wild arXiv preprint arXiv:2105.09824, 2021 | 5 | 2021 |
| CFD Study of an Over-Wing Nacelle Configuration SH Berguin, SA Renganathan, J Ahuja, M Chen, C Perron, J Tai, ... https://smartech.gatech.edu/handle/1853/60464, 2018 | 4 | 2018 |
| CAMERA: A method for cost-aware, adaptive, multifidelity, efficient reliability analysis SA Renganathan, V Rao, IM Navon Journal of Computational Physics 472, 111698, 2023 | 3 | 2023 |
| Wild. Lookahead acquisition functions for finite-horizon time-dependent Bayesian optimization and application to quantum optimal control SA Renganathan, J Larson, M Stefan arXiv preprint arXiv:2105.09824, 2021 | 3 | 2021 |
| Cluster analysis of wind turbine wakes measured through a scanning Doppler wind LiDAR R Maulik, V Rao, SA Renganathan, S Letizia, GV Iungo AIAA Scitech 2021 Forum, 1181, 2021 | 3 | 2021 |
| Validation and assesment of lower order aerodynamics based design of ram air turbines A Renganathan, RK Denney, A Duquerrois, DN Mavris 12th International Energy Conversion Engineering Conference, 3463, 2014 | 3 | 2014 |
| Recursive two-step lookahead expected payoff for time-dependent bayesian optimization SA Renganathan, J Larson, S Wild arXiv preprint arXiv:2006.08037, 2020 | 2 | 2020 |
Dimitri N. MavrisGeorgia Institute of TechnologyVerified email at aerospace.gatech.edu
