A radial basis function-based multi-fidelity surrogate model: exploring correlation between high-fidelity and low-fidelity models

Song, Xueguan; Lv, Liye; Sun, Wei; Zhang, Jie

doi:10.1007/s00158-019-02248-0

Cited by 118 publications

(29 citation statements)

References 27 publications

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“…In the literature, the correlation coefficient is usually used to measure the correlation between LF and HF responses in the testing problems (cf. [41]). However, the correlation coefficient actually is the cosine similarity between the centered LF and HF response data, and thus cannot describe the intrinsic relatedness between LF and HF responses.…”

Section: Numerical Experimentsmentioning

confidence: 99%

GAN-MDF: A Method for Multi-fidelity Data Fusion in Digital Twins

Liu¹,

Zhang²,

Tao³

2021

Preprint

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The Internet of Things (IoT) collects real-time data of physical systems, such as smart factory, intelligent robot and healtcare system, and provide necessary support for digital twins. Depending on the quality and accuracy, these multi-source data are divided into different fidelity levels. Highfidelity (HF) responses describe the system of interest accurately but are computed costly. In contrast, low-fidelity (LF) responses have a low computational cost but could not meet the required accuracy.Multi-fidelity data fusion (MDF) methods aims to use massive LF samples and small amounts of HF samples to develop an accurate and efficient model for describing the system with a reasonable computation burden. In this paper, we propose a novel generative adversarial network for MDF in digital twins (GAN-MDF). The generator of GAN-MDF is composed of two sub-networks: one extracts the LF features from an input; and the other integrates the input and the extracted LF features to form the input of the subsequent discriminator. The discriminator of GAN-MDF identifies whether the generator output is a real sample generated from HF model. To enhance the stability

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Section: Numerical Experimentsmentioning

confidence: 99%

GAN-MDF: A Method for Multi-fidelity Data Fusion in Digital Twins

Liu¹,

Zhang²,

Tao³

2021

Preprint

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“…In recent years, surrogate model (SM) technology has been extensively used in the field of multidisciplinary design [12]. The SM can express the relationship between a set of input variables and output variables through a mathematical model [13][14][15]. Under the action of the correlation function, the SM has the characteristics of local estimation, which makes it easy to obtain ideal fitting results when solving highly non-linear problems [16].…”

Section: Introductionmentioning

confidence: 99%

Multi‐objective optimisation of the HSPMM rotor based on the multi‐physics surrogate model

Dai

Zhang

Wang

et al. 2021

IET Electric Power Appl

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High-speed permanent magnetic machine (HSPMM) is attracting more attention due to its high power density, compact size, small rotating inertia, and rapid response capability. However, the design of the HSPMM rotor is a non-linear, multi-physics coupled process that makes it difficult to build an accurate mathematical model for optimisation. This study proposes a multi-objective optimisation method based on the multi-physics surrogate model (MPSM). This method uses an MPSM to replace the finite element model (FEM) for optimisation, which can effectively solve the problem of non-convergence and time consumption of the traditional FEM in the optimisation process. Finally, a 1.1 MW, 18,000 r/min HSPMM is produced and related experiments are carried out; the feasibility of the method proposed in this study for HSPMM optimisation is verified.

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“…In the first strategy, a variablefidelity surrogate model is built to minimize the discrepancy between the LF model and the HF model. A lot of previous studies discussed how to build accurate variable-fidelity models (Zheng et al 2015;Zhou et al 2017;Durantin et al 2017;Liu et al 2018;Zheng 2018;Song et al 2019). Zhang et al (2018) proposed a variable-fidelity expected improvement criterion on a hierarchical Gaussian process model, which can adaptively select new samples on both LF and HF models.…”

Section: Introductionmentioning

confidence: 99%

A multi-fidelity RBF surrogate-based optimization framework for computationally expensive multi-modal problems with application to capacity planning of manufacturing systems

Shen

Shoemaker

2020

Struct Multidisc Optim

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This paper presents a multi-fidelity RBF (radial basis function) surrogate-based optimization framework (MRSO) for computationally expensive multi-modal optimization problems when multi-fidelity (high-fidelity (HF) and low-fidelity (LF)) models are available. The HF model is expensive and accurate while the LF model is cheaper to compute but less accurate. To exploit the correlation between the LF and HF models and improve algorithm efficiency, in MRSO, we first apply the DYCORS (dynamic coordinate search algorithm using response surface) algorithm to search on the LF model and then employ a potential area detection procedure to identify the promising points from the LF model. The promising points serve as the initial start points when we further search for the optimal solution based on the HF model. The performance of MRSO is compared with 6 other surrogate-based optimization methods (4 are using a single-fidelity surrogate and the rest 2 are using multi-fidelity surrogates). The comparisons are conducted on a multi-fidelity optimization test suite containing 10 problems with 10 and 30 dimensions. Besides the benchmark functions, we also apply the proposed algorithm to a practical and computationally expensive capacity planning problem in manufacturing systems which involves discrete event simulations. The experimental results demonstrate that MRSO outperforms all the compared methods.

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A radial basis function-based multi-fidelity surrogate model: exploring correlation between high-fidelity and low-fidelity models

Cited by 118 publications

References 27 publications

GAN-MDF: A Method for Multi-fidelity Data Fusion in Digital Twins

GAN-MDF: A Method for Multi-fidelity Data Fusion in Digital Twins

Multi‐objective optimisation of the HSPMM rotor based on the multi‐physics surrogate model

A multi-fidelity RBF surrogate-based optimization framework for computationally expensive multi-modal problems with application to capacity planning of manufacturing systems

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