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), 2020 | 73 | 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 | 65 | 2021 |
Aerodynamic data fusion toward the digital twin paradigm SA Renganathan, K Harada, DN Mavris AIAA journal 58 (9), 3902-3918, 2020 | 36 | 2020 |
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 | 32 | 2022 |
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 | 26 | 2020 |
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 | 25 | 2021 |
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 | 21 | 2018 |
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 | 17 | 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 |
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 |
CAMERA: A method for cost-aware, adaptive, multifidelity, efficient reliability analysis SA Renganathan, V Rao, IM Navon Journal of Computational Physics 472, 111698, 2023 | 12 | 2023 |
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 |
Experimental investigation on the effect of microstructure modifiers and heat treatment influence on A356 alloy J Baskaran, P Raghuvaran, S Ashwin Materials Today: Proceedings 37, 3007-3010, 2021 | 11 | 2021 |
Machine-learning identification of the variability of mean velocity and turbulence intensity for wakes generated by onshore wind turbines: Cluster analysis of wind LiDAR … GV Iungo, R Maulik, SA Renganathan, S Letizia Journal of Renewable and Sustainable Energy 14 (2), 2022 | 10 | 2022 |
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 | 10 | 2018 |
Multifidelity Gaussian processes for failure boundary and probability estimation A Renganathan, V Rao, I Navon AIAA Scitech 2022 Forum, 0390, 2022 | 9 | 2022 |
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 | 9 | 2021 |
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 | 7 | 2014 |
Sensitivity analysis of the overwing nacelle design space J Ahuja, S Ashwin Renganathan, DN Mavris Journal of Aircraft 59 (6), 1478-1492, 2022 | 6 | 2022 |
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 | 5 | 2014 |