18: Multi-Fidelity, Parallel Bayesian Optimization with Expensive Simulators

Music City Center 

Computer simulation plays a central role in modern design of physical experiments and engineered systems. However, the computational expense of high-fidelity simulation limits the throughput for searching through potential design spaces for optimal and novel cases. Bayesian optimization (BO) is a common approach to making this process more efficient by leveraging the predictive power of machine learning. In this work, we advance BO to use evaluations from multiple physical simulations to multi-fidelity BO and do so with asynchronous, parallel evaluation. This allows us to autonomously use fast, lower accuracy models to broadly search the design space and thoughtfully use more expensive, more high-fidelity simulations in the most promising subsets of design space. We demonstrate our results on optimal design of an inertial confinement fusion capsule.