A novel adaptive quality-based multi-fidelity surrogate framework for multiple low-fidelity data sources

In this paper, a novel adaptive quality-based multi-fidelity (AQBMF) surrogate framework is introduced to maximize the utilization of low-fidelity (LF) data from various domains. The main goal of the proposed method is to adaptively select and combine LF data, by assessing its quality, to create the most accurate surrogate. The core idea lies in interpreting the quality levels of LF data sources as the relative importance of LF surrogates that serve as basis functions in a multi-fidelity (MF) surrogate. Based on this approach, the proposed AQBMF surrogate framework comprises four main stages. In the first stage, a newly defined augmented MF formulation is constructed, initially assuming equal importance for all LF data sources. In the second stage, LF surrogates are ranked by importance through the proposed MF basis screening method. In the third stage, promising candidate surrogates are systematically constructed based on the importance ranking of the LF surrogates. During this stage, both the selection and filtering of LF data, as well as the hierarchical and ensemble combination-based MF methods are considered. In the last stage, the best surrogate is selected from the candidates using the proposed algorithm. Various benchmark test results demonstrate the superior performance of the proposed framework. Finally, engineering application results show that the proposed AQBMF surrogate achieves higher accuracy than existing ones within the same computational budget.