2 papers
stat.ML2026
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…
math.OC2025
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…