Study on the Performance of a Multi-fidelity Meta-model with Various Low-fidelity Models

Recently, many researchers have studied multi-fidelity meta-models to efficiently carry out design optimization using CAE. Single fidelity meta-model, built based on high fidelity data only, usually requires ten times the number of design variables to reasonably approximate the exact model, whose computational burden can be heavy for the problems requiring long analysis time. However, a multi-fidelity meta-model is built based on the combination of low fidelity data and high fidelity data. In the multi-fidelity meta-model, high fidelity data are highly accurate but expensive to evaluate, while low fidelity data are cheap to evaluate but have a low accuracy compared to the high fidelity data. Multi-fidelity meta-model can efficiently approximate the exact model by using a small number of high fidelity data with a large number of low fidelity data. In this study, we studied the performance of a multi-fidelity meta-model with varying the number of samples and a variety of low fidelity models for eight mathematical problems. From the results, we proposed guidelines for the number of samples and trend of the low fidelity model.??? ????????? 2013?????? ??????(?????????????????????)??? ???????????? ????????????????????? ????????? ?????? ????????? ?????????(NRF-2011-0016701)