A multi-fidelity surrogate modeling method in the presence of non-hierarchical low-fidelity data

Abstract

Multi-fidelity (MF) surrogate models are increasingly utilized in engineering design due to their capability to achieve desired accuracy at a reduced cost. However, many existing MF modeling methods presuppose a hierarchical relationship between different fidelity models, which may not be applicable when multiple low-fidelity (LF) models exhibit varying fidelity levels across the design space due to different simplification methods. To tackle this issue, a multi-fidelity surrogate modeling method based on local correlation-weighted fusion (LCWF-MFS) is developed. In this proposed approach, each LF model is assigned variable weights depending on its local correlation with the high-fidelity (HF) model, thereby maximizing the utilization of information from these LF data sources. Furthermore, an innovative parameter tuning technique rooted in penalty error optimization is introduced, aiming to establish a more reliable scale factor between HF and LF models. Several numerical examples as well as a maximum deformation prediction problem pertaining to the micro aerial vehicle (MAV) fuselage during landing or impact are used to illustrate the effectiveness of the proposed method. Results demonstrate the superior accuracy and robustness of the proposed LCWF-MFS method in comparison to alternative methods.