A multi-fidelity surrogate model by optimal model selection

Tong Liu; Xiaonan Lai; Xueguan Song; Zhenggang Guo

doi:10.1117/12.2686535

10 August 2023 A multi-fidelity surrogate model by optimal model selection

Tong Liu, Xiaonan Lai, Xueguan Song, Zhenggang Guo

Proceedings Volume 12759, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023); 127593F (2023) https://doi.org/10.1117/12.2686535
Event: 2023 3rd International Conference on Automation Control, Algorithm and Intelligent Bionics (ACAIB 2023), 2023, Xiamen, China

Abstract

Engineering problems typically involve complex structures and physical processes, which often make them difficult to solve directly. To address these challenges, surrogate models have become a common approach for approximating and optimizing engineering systems. Although high-fidelity (HF) models offer higher accuracy, collecting HF data points and performing calculations can be very challenging due to their high cost and computational complexity. In contrast, low-fidelity (LF) data points are easier to obtain, but their computational accuracy is relatively low. Therefore, developing multi-fidelity surrogate (MFS) models has become a necessary research direction. In this paper, we propose a multi-fidelity surrogate model by optimal model selection (MFS-OMS). The optimal model (OM) that reflects the difference between HF and LF is selected by combining cross-validation (CV) and mean squared error (MSE), and the proportion factor between high-fidelity and low-fidelity models is calculated using genetic algorithm. MFS-OMS is compared with Co_Kriging and LR_MFS using numerical examples and a press machine optimization problem. The experimental results show that MFS_OMS can provide more accurate prediction accuracy than Co_Kriging and LR_MFS models. The research in this paper can promote the development and improvement of models and algorithms, and provide more possibilities and feasibility for the application of surrogate models in a wider range of fields.

Citation Download Citation

Tong Liu, Xiaonan Lai, Xueguan Song, and Zhenggang Guo "A multi-fidelity surrogate model by optimal model selection", Proc. SPIE 12759, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023), 127593F (10 August 2023); https://doi.org/10.1117/12.2686535

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KEYWORDS

Design and modelling

Performance modeling

Education and training

Statistical modeling

Cross validation

Data modeling

Engineering

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