S. Ashwin Renganathan
S. Ashwin Renganathan
Assistant Professor, The Pennsylvania State University
Verified email at - Homepage
Cited by
Cited by
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
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
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
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
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
Aerodynamic data fusion toward the digital twin paradigm
SA Renganathan, K Harada, DN Mavris
AIAA Journal 58 (9), 3902-3918, 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
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
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
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
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
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
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
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
CFD Study of an Over-Wing Nacelle Configuration
SH Berguin, SA Renganathan, J Ahuja, M Chen, C Perron, J Tai, ..., 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
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
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
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
Recursive two-step lookahead expected payoff for time-dependent bayesian optimization
SA Renganathan, J Larson, S Wild
arXiv preprint arXiv:2006.08037, 2020
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