Efficient Simulation Sampling Allocation Using Multifidelity Models

Abstract

Simulation is often used to estimate the performance of alternative system designs for selecting the best. For a complex system, high-fidelity simulation is usually time-consuming and expensive. In this paper, we provide a new framework that integrates information from the multifidelity models to increase efficiency for selecting the best. A Gaussian mixture model is introduced to capture performance clustering information in the multifidelity models. Posterior information obtained by a clustering analysis incorporates both cluster-wise information and idiosyncratic information for each design. We propose a new budget allocation method to efficiently allocate high-fidelity simulation replications, utilizing posterior information. Numerical experiments show that the proposed multifidelity framework achieves a significant boost in efficiency.