A multi-fidelity surrogate model based on extreme support vector regression: fusing different fidelity data for engineering design. Issue 2 (13th April 2023)

• Record Type: Journal Article
• Authors: Shi, Mao-Lin; Lv, Liye; Xu, Lizhang
• Is Part Of: Engineering computations. Volume 40:Issue 2(2023)
• Page Start: 473
• Page End: 493
• Publication Date: 2023-04-13
• Subjects: Multi-fidelity surrogate model -- Extreme support vector regression -- Metamodeling; Computer-aided engineering -- Periodicals; Computer graphics -- Periodicals; 620.00285
• DOI: 10.1108/EC-10-2021-0583
• Languages: English
• ISSNs: 0264-4401

Abstract

Abstract : Purpose: Extreme support vector regression (ESVR) has been widely used in the design, analysis and optimization of engineering systems of its fast training speed and good computational ability. However, the ESVR model is only able to utilize one-fidelity information of engineering system. To solve this issue, this paper extends extreme support vector regression (ESVR) to a multi-fidelity surrogate (MFS) model which can make use of a few expensive but higher-fidelity (HF) samples and a lot of inaccurate but cheap low-fidelity (LF) samples, named ESVR-MFS. Design/methodology/approach: In the ESVR-MFS model, a kernel matrix is designed to evaluate the relationship between the HF and LF samples. The root mean square error of HF samples is used as the training error metric, and the optimal hyper-parameters of the kernel matrix are obtained through a heuristic algorithm. Findings: A number of numerical problems and three engineering problems are used to compare the ESVR-MFS model with the single-fidelity ESVR model and two benchmark MFS models. The results show that the ESVR-MFS model exhibits competitive performance in both numerical cases and practical cases tested in this work. Practical implications: The proposed approach exhibits great capability for practical multi-fidelity engineering design problems. Originality/value: A MFS model is proposed based on ESVR, which can make full use of the advantages of both HF data and LF data to achieve optimal results at same or lower cost.