Ensemble learning based hierarchical surrogate model for multi-fidelity information fusion

Recently, multi-fidelity information fusion based surrogate modeling methods have made great progress in the engineering design and optimization tasks. Two main issues in this field are: (1) Since the overarching trend of the responses from the high-fidelity (HF) model is captured by the low-fidelity (LF) model, inaccurate LF model may negatively impact the final modeling results. (2) The responsiveness of multi-fidelity surrogate (MFS) models to variations in the relationship between LF and HF models leads to limited prediction performance. To this end, we propose an ensemble learning based MFS modeling method with a hierarchical framework, called EL-MFS. Specifically, to alleviate the impact of issue (1), we present an adaptive ensemble surrogate model, which aims to effectively mitigate the negative impact of inappropriate LF model selection on the HF approximation results. Furthermore, we propose to exploit the feature mapping and hierarchical framework to boost the versatility of the model as a way to mitigate the dependence of MFS performance on the relationship between HF and LF models. To assess the effectiveness of the proposed model, a sequence of numerical problems is tested, and some advanced surrogates are selected as the baseline models. Moreover, to demonstrate the potential of the proposed model in aiding intricate engineering design, one engineering case is investigated as well.