4 papers · 1 filter
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…