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Hierarchical Multi-fidelity Surrogate Modeling

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Multi-fidelity Surrogates

Part of the book series: Engineering Applications of Computational Methods ((EACM,volume 12))

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Abstract

Most of the existing multi-fidelity (MF) surrogates assume that high-fidelity (HF) models are generally more accurate than low-fidelity (LF) models but LF models are less expensive than HF models. In other words, the fidelity levels between the HF and LF models can be clearly identified, which is the core concept of hierarchical multi-fidelity surrogate modeling. The motivation of MF surrogate modeling is that a large number of inexpensive LF sampling points can be used to decrease the computational cost, while a limited number of expensive HF sampling points can be used to ensure the prediction accuracy of the surrogate model.

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Authors and Affiliations

  1. School of Aerospace Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China

    Qi Zhou

  2. China Academy of Launch Vehicle Technology, Bei**g, China

    Min Zhao

  3. School of Aerospace Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China

    Jiexiang Hu

  4. China Academy of Launch Vehicle Technology, Bei**g, China

    Mengying Ma

Authors
  1. Qi Zhou
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  2. Min Zhao
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  4. Mengying Ma
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Correspondence to Qi Zhou .

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Zhou, Q., Zhao, M., Hu, J., Ma, M. (2023). Hierarchical Multi-fidelity Surrogate Modeling. In: Multi-fidelity Surrogates. Engineering Applications of Computational Methods, vol 12. Springer, Singapore. https://doi.org/10.1007/978-981-19-7210-2_2

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  • DOI: https://doi.org/10.1007/978-981-19-7210-2_2

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-7209-6

  • Online ISBN: 978-981-19-7210-2

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