Multi-Source Surrogate Modeling with Bayesian Hierarchical Regression

摘要

The early phases of design of unconventional aerospace systems often lacks historical data and accurate low-fidelity analyses. Accurate data from sources such as flight tests, wind tunnel tests, and computational experiments are required to enable accurate predictions of system performance through out the design space. Due to the significant resources required by these physical and computational experiments, high-fidelity data are sparsely distributed throughout the design space. This paper proposes a multi-source surrogate modeling method using Bayesian hierarchical regression to build an accurate regression model by combining sparse information from different sources. This paper illustrates the method using one dimensional analytic functions for two different scenarios: 1) when all of the sources are at same level of fidelity and 2) when sources have different levels of fidelity. Finally, the method is demonstrated on an airfoil drag analysis problem in which a regression model on sparse wind tunnel data is improved using data from two moderate fidelity computer programs.