Talks & Publications

Seminar/Conference Talks

  1. "Lookahead Bayesian optimization and applications", SIAM Conference on Uncertainty Quant. (4/12-4/15/22, Atlanta, GA).

  2. "Lookahead Bayesian optimization and applications", Invited Talk, Session: Derivative-free/blackbox/simulation-based optimization, Seventh International Conference on Continuous Optimization (ICCOPT). (7/25 - 7/28/2022, Bethlehem, PA).

  3. "Multifidelity Gaussian processes for failure boundary and probability estimation", AIAA SciTech Conference, 1/3 - 1/7/2022, San Diego, CA.

  4. ”Lookahead Bayesian Optimization for Quantum Optimal Control”, Invited Talk, SIAM Conference on Optimization 2021 (OP21), July 2021, Spokane, WA.

  5. From Data to Decisions in the Design of Complex Engineered Systems”, Invited Talk, Probabilistic Design Seminar, General Electric Research, April 2021, Niskayuna, NY.

  6. ”Lookahead Bayesian Optimization for Quantum Optimal Control”, Invited Talk, SIAM Conference on Computational Science and Engineering (CSE21) , March 2021, Fort Worth, TX.

  7. ”Advanced Methods for Design and Control of Com-plex Engineered Systems”, Invited Talk, Core-Artificial Intelligence/Machine Learning (AI/ML) Research Group Seminar, Ford Motor Company, February 2021, Dearborn, MI.

  8. "Bayesian Optimization of Expensive Oracles with Input-dependent, Correlated Noise", ANL Postdoctoral Symposium 2019, 11/7/2019, Lemont, IL

  9. "Multifidelity Data Fusion via Bayesian Inference", MDO-16: Non-Deterministic Design Methods and Applications, AIAA Aviation Forum & Exposition, 6/17 - 6/21 2019, Dallas, TX

  10. "An Adaptive Sampling Approach for Surrogate Modeling of Expensive Computer Experiments", Machine Learning in Science & Engineering (MLSE) Conference, 6/10 - 6/12 2019, Atlanta, GA

  11. "Bayesian global optimization of expensive, strongly non-stationary, black-box functions", 2019 IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization, 5/29 - 5/31 2019, Boston, MA.

  12. "An Adaptive Sampling Approach for Surrogate Modeling of Expensive Computer Experiments", SIAM CSE19, 2/25 - 3/1 2019, Spokane, WA.

  13. "Towards faster, cheaper and reliable aircraft design", Invited Talk, Mathematics & Computer Science Division, 2/8/2019, Argonne National Laboratory, Lemont, IL.

  14. "An Adaptive Sampling Approach for Surrogate Modeling of Expensive Computer Experiments", Invited Talk, Applied and Computational Mathematics Seminar, School of Mathematics, 2/4/2019, Atlanta, GA.

  15. "Towards faster, cheaper and reliable aircraft design", Invited Talk, Computational Research Division, 1/11/2019, Lawrence Berkeley National Laboratory, Berkeley, CA.

  16. "Towards faster, cheaper and reliable aircraft design", 2018 Postdoctoral Symposium, Georgia Institute of Technology, 9/2018, Atlanta, GA.

  17. “Sensitivity Analysis of Aero-Propulsive Coupling for Over-Wing-Nacelle Concepts”, 2018 AIAA Aerospace Sciences Meeting, AIAA SciTech Forum, (AIAA 2018-1757)

  18. "Multidisciplinary Analysis of Aerodynamics-Propulsion Coupling for the OWN Concept", 2018 Multidisciplinary Analysis and Optimization Conference, AIAA AVIATION Forum, (AIAA 2018-2927)

  19. “A Methodology for Projection-Based Model Reduction with Black-Box High-Fidelity Models”, 17th AIAA Aviation Technology, Integration, and Operations Conference (2017), Denver, CO. arXiv

  20. "Overview of Reduced Order Modeling", Advanced Design Methods – III Guest Lecture, GeorgiaTech, Spring 2017

  21. ”Sequential Experimental Design using Bayesian Inference“, Bayesian Statistics Final Project, Spring 2016

  22. “Sequential Extraction of Orthonormal Bases“, Numerical Linear Algebra, Final Project, Spring 2016.

  23. “Conceptual Design of a 2-stage runway based space launch system”, 51st AIAA/SAE/ASEE Joint Propulsion Conference. July 2015, Orlando, FL

  24. “Validation and Assesment of Lower Order Aerodynamics Based Design of Ram Air Turbines”, 12th International Energy Conversion Engineering Conference. July 2014, Cleveland, OH

Journal Articles

  1. Renganathan, S. A., Romit Maulik, and Jai Ahuja. (2021) "Enhanced data efficiency using deep neural networks and Gaussian processes for aerodynamic design optimization." (forthcoming) Aerospace Science and Technology.

  2. Renganathan, S.A., Larson, J.M. and Wild, S., "Recursive Two-Step Lookahead Expected Payoff for Time-Dependent Bayesian Optimization", (submitted) arXiv

  3. Rajaram, D, Puranik, T., Renganathan, S.A., et al, (2020) ”Empirical As-sessment of Deep Gaussian Process Surrogate Models for Engineering Problems”. AIAA Journal of Aircraft, https://doi.org/10.2514/1.C036026

  4. Renganathan, S. A., Kohei Harada, and Dimitri N. Mavris. (2020) "Aerodynamic Data Fusion Toward the Digital Twin Paradigm." AIAA Journal 58 (9), 3902-3918

  5. Renganathan, S. A., Maulik, R., & Rao, V. (2020). "Machine learning for nonintrusive model order reduction of the parametric inviscid transonic flow past an airfoil." Physics of Fluids, 32(4), 047110.

  6. Renganathan, S. A. "Koopman-based approach to nonintrusive reduced order modeling: Application to aerodynamic shape optimization and uncertainty propagation." AIAA Journal 58, no. 5 (2020): 2221-2235.

  7. Renganathan, S. A., Yingjie Liu, and Dimitri N. Mavris. "Koopman-based approach to nonintrusive projection-based reduced-order modeling with black-box high-fidelity models." AIAA Journal 56, no. 10 (2018): 4087-4111.

  8. Mishra, D.P., Renganathan, S.A., (2014) "Numerical Study of Flame/Vortex Interactions in a 2-D Trapped Vortex Combustor", THERMAL SCIENCE: (2014), Vol. 18, No. 4, pp. 1373-1387

  9. Mishra, D.P. and Renganathan, S.A. (2010),"Numerical analysis of fuel—air mixing in a two-dimensional trapped vortex combustor", Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 224(1), 65–75.

Conference Proceedings (refereed)

  1. Maulik, R., Rao, V., Renganathan, S. A., Letizia, S., & Iungo, G. V. (2021). Cluster analysis of wind turbine wakes measured through a scanning Doppler wind LiDAR. In AIAA Scitech 2021 Forum (p. 1181).

  2. Rajaram, Dushhyanth, Tejas G. Puranik, Renganathan, S.A., et al. "Deep Gaussian Process Enabled Surrogate Models for Aerodynamic Flows." In AIAA Scitech 2020 Forum, p. 1640. 2020.

  3. Renganathan, S.A., Harada, K., & Mavris, D. N. (2019). Multifidelity Data Fusion via Bayesian Inference. In AIAA Aviation 2019 Forum (p. 3556).

  4. Renganathan, S.A., Steven H. Berguin, Mengzhen Chen, Jai Ahuja, Jimmy C. Tai, Dimitri N. Mavris, and David Hills. “Sensitivity Analysis of Aero-Propulsive Coupling for Over-Wing-Nacelle Concepts”, 2018 AIAA Aerospace Sciences Meeting, AIAA SciTech Forum, (AIAA 2018-1757)

  5. Jai Ahuja, Renganathan, S.A., Steven Berguin and Dimitri N. Mavris, "Multidisciplinary Analysis of Aerodynamics-Propulsion Coupling for the OWN Concept", 2018 Multidisciplinary Analysis and Optimization Conference, AIAA AVIATION Forum, (AIAA 2018-2927)

  6. Renganathan, S.A., Dimitri N Mavris, “A Methodology for Projection-Based Model Reduction with Black-Box High-Fidelity Models”, 17th AIAA Aviation Technology, Integration, and Operations Conference (2017), Denver, CO. arXiv

  7. Renganathan, S.A. and D.Mavris, “Conceptual Design of a 2-stage runway based space launch system”, 51st AIAA/SAE/ASEE Joint Propulsion Conference. July 2015, Orlando, FL

  8. Renganathan, S.A., R.Denney, A.Duquerrois and D.Mavris, “Validation and Assesment of Lower Order Aerodynamics Based Design of Ram Air Turbines”, 12th International Energy Conversion Engineering Conference. July 2014, Cleveland, OH

  9. Renganathan, S.A., Numerical study of flame-vortex interactions in a Trapped Vortex Combustor, (Poster), Proceedings of the Combustion Institute (2008), Montreal, Canada.

Theses & Reports

  1. Berguin, S.H., Renganathan,S.A. et al, (2018) "CFD Study of an Over-Wing Nacelle Configuration", Technical Report 2018, Georgia Institute of Technology.

  2. Renganathan, S. A. (2018). A Methodology for Non-Intrusive projection-based model reduction of expensive black-box PDE-based systems and application in the many-query context (Doctoral Dissertation)