66 citations · 115 across the 12 of their papers we have counts for
12 papers
Two-stage Design for Failure Probability Estimation with Gaussian Process Surrogates
Annie S. Booth, S. Ashwin Renganathan
We tackle the problem of quantifying failure probabilities for expensive deterministic computer experiments with stochastic inputs under a fixed budget. The computational cost of t…
Multifidelity Cross-validation
S. Ashwin Renganathan, Kade Carlson
Emulating the mapping between quantities of interest and their control parameters using surrogate models finds widespread application in engineering design, including in numerical…
Contour Location for Reliability in Airfoil Simulation Experiments using Deep Gaussian Processes
Annie S. Booth, S. Ashwin Renganathan, Robert B. Gramacy
Bayesian deep Gaussian processes (DGPs) outperform ordinary GPs as surrogate models of complex computer experiments when response surface dynamics are non-stationary, which is espe…
CAMERA: A Method for Cost-aware, Adaptive, Multifidelity, Efficient Reliability Analysis
S. Ashwin Renganathan, Vishwas Rao, Ionel M. Navon
Estimating probability of failure in aerospace systems is a critical requirement for flight certification and qualification. Failure probability estimation involves resolving tails…
Machine-learning identification of the variability of mean velocity and turbulence intensity for wakes generated by onshore wind turbines: Cluster analysis of wind LiDAR measurements
G. Valerio Iungo, Romit Maulik, S. Ashwin Renganathan +1
Wind turbine wakes are the result of the extraction of kinetic energy from the incoming atmospheric wind exerted from a wind turbine rotor. Therefore, the reduced mean velocity and…
Data-Driven Wind Turbine Wake Modeling via Probabilistic Machine Learning
S. Ashwin Renganathan, Romit Maulik, Stefano Letizia +1
Wind farm design primarily depends on the variability of the wind turbine wake flows to the atmospheric wind conditions, and the interaction between wakes. Physics-based models tha…
Lookahead Acquisition Functions for Finite-Horizon Time-Dependent Bayesian Optimization and Application to Quantum Optimal Control
S. Ashwin Renganathan, Jeffrey Larson, Stefan M. Wild
We propose a novel Bayesian method to solve the maximization of a time-dependent expensive-to-evaluate stochastic oracle. We are interested in the decision that maximizes the oracl…
Enhanced data efficiency using deep neural networks and Gaussian processes for aerodynamic design optimization
S. Ashwin Renganathan, Romit Maulik and, Jai Ahuja
Adjoint-based optimization methods are attractive for aerodynamic shape design primarily due to their computational costs being independent of the dimensionality of the input space…
Recursive Two-Step Lookahead Expected Payoff for Time-Dependent Bayesian Optimization
S. Ashwin Renganathan, Jeffrey Larson, Stefan Wild
We propose a novel Bayesian method to solve the maximization of a time-dependent expensive-to-evaluate oracle. We are interested in the decision that maximizes the oracle at a fini…
Aerodynamic Data Fusion Towards the Digital Twin Paradigm
S. Ashwin Renganathan, Kohei Harada, Dimitri N. Mavris
We consider the fusion of two aerodynamic data sets originating from differing fidelity physical or computer experiments. We specifically address the fusion of: 1) noisy and in-com…
Koopman-Based Approach to Non-intrusive Projection-Based Reduced-Order Modeling with Black-Box High-Fidelity Models. Part II: Application
S. Ashwin Renganathan
A methodology for non-intrusive, projection-based non-linear model reduction originally presented by Renganathan et. al. (2018)~\cite{renganathan2018koopman} is further extended to…
A Methodology for Projection-Based Model Reduction with Black-Box High-Fidelity Models
S. Ashwin Renganathan, Yingjie Liu, Dimitri N. Mavris
This paper presents a methodology that enables projection-based model reduction for black-box high-fidelity models such as commercial CFD codes. The methodology specifically addres…