A Bayesian Optimization Framework for Personalized System Design Based on Computer Experiments

Music City Center 

Personalized system design incorporates human characteristics into optimization to improve system responses. We propose a new Bayesian Optimization (BO) framework that develops a continuous design policy, which assigns optimal designs based on human covariates and minimizes population-wise expected responses. Current methods rely heavily on observational data and discrete designs, ignoring population and design variations. Traditional BO faces challenges in developing reliable surrogate models, requiring extensive space-filling simulations and often failing in individual response prediction. Our proposed BO method addresses these issues by using distinct objective functions across three sub-steps: training a Gaussian process surrogate, optimizing the design policy, and efficiently introducing new samples through a new acquisition function, Personalized Information Gain (PIG). This function focuses on informative simulation runs to reduce uncertainty and improve computational efficiency by searching along the optimal design policy. Numerical examples using synthetic data and vehicle restraint system design demonstrate the effectiveness and robustness of the proposed method.

Bayesian Optimization

Personalized System Design

Vehicle Restraint System Design 

Transportation Statistics Interest Group