S. Ashwin Renganathan
S. Ashwin Renganathan
Argonne National Laboratory
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Citado por
Citado por
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
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
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
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
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
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
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
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
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
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
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
Multifidelity Data Fusion via Bayesian Inference
A Renganathan, K Harada, DN Mavris
AIAA Aviation 2019 Forum, 3556, 2019
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
A Methodology for Projection-Based Model Reduction with Black-Box High-Fidelity Models
A Renganathan, DN Mavris
17th AIAA Aviation Technology, Integration, and Operations Conference, 4444, 2017
Aerodynamic Data Fusion Toward the Digital Twin Paradigm
SA Renganathan, K Harada, DN Mavris
AIAA Journal 58 (9), 3902-3918, 2020
Recursive Two-Step Lookahead Expected Payoff for Time-Dependent Bayesian Optimization
SA Renganathan, J Larson, S Wild
arXiv preprint arXiv:2006.08037, 2020
Koopman-Based Approach to Non-intrusive Projection-Based Reduced-Order Modeling with Black-Box High-Fidelity Models. Part II: Application
SA Renganathan
arXiv preprint arXiv:1811.05765, 2018
Multidisciplinary analysis of aerodynamics-propulsion coupling for the OWN concept
J Ahuja, A Renganathan, S Berguin, DN Mavris
2018 Multidisciplinary Analysis and Optimization Conference, 2927, 2018
Data-Driven Deep Learning Emulators for Geophysical Forecasting
VK Sastry, R Maulik, V Rao, B Lusch, SA Renganathan, R Kotamarthi
International Conference on Computational Science, 433-446, 2021
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