8 papers
Learning What to Evaluate: Correlation-Aware Decoupling for Multiobjective Bayesian Optimization
Ashwin Renganathan, Peter Bachman
Multiobjective Bayesian optimization (MOBO) with Gaussian process (GP) surrogates is a sample efficient approach to solving multiobjective optimization problems. In MOBO, a Bayesia…
A Bayesian latent Gaussian process framework for aerodynamic uncertainty quantification
Geoffrey Davis, Ashwin Renganathan
Predicting the aerodynamic performance (e.g. lift, drag, and moment coefficients) of an aircraft is challenging -- computational models are biased and direct simulations are prohib…
REMAL: Residual Equilibrium Manifold Active Learning for Surrogate-Based Multidisciplinary Design Analysis
Kail Yuan, Ashwin Renganathan
Multidisciplinary design analysis of coupled engineering systems requires the computation of equilibrium states in which all disciplinary coupling variables are mutually consistent…
Surrogate-Guided Adaptive Importance Sampling for Failure Probability Estimation
Ashwin Renganathan, Annie S. Booth
We consider the sample efficient estimation of failure probabilities from expensive oracle evaluations of a limit state function via importance sampling (IS). In contrast to conven…
: Convolutional Regularized Least Squares Framework for Reduced Order Modeling of Transonic Flows
Muhammad Bilal, Ashwin Renganathan
We develop a convolutional regularized least squares () framework for reduced-order modeling of transonic flows with shocks. Conventional proper orthogonal decomposi…
Multiobjective Aerodynamic Design Optimization of the NASA Common Research Model
Kade Carlson, Ashwin Renganathan
Aircraft aerodynamic design optimization must account for the varying operating conditions along the cruise segment as opposed to designing at one fixed operating condition, to arr…
Derivative-free optimization is competitive for aerodynamic design optimization in moderate dimensions
Punya Plaban, Peter Bachman, Ashwin Renganathan
Aerodynamic design optimization is an important problem in aircraft design that depends on the interplay between a numerical optimizer and a high-fidelity flow physics solver. Deri…
qPOTS: Efficient batch multiobjective Bayesian optimization via Pareto optimal Thompson sampling
Ashwin Renganathan, Kade E. Carlson
Classical evolutionary approaches for multiobjective optimization are quite accurate but incur a lot of queries to the objectives; this can be prohibitive when objectives are expen…