IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v323y2018icp120-131.html
   My bibliography  Save this article

Extended Co-Kriging interpolation method based on multi-fidelity data

Author

Listed:
  • Xiao, Manyu
  • Zhang, Guohua
  • Breitkopf, Piotr
  • Villon, Pierre
  • Zhang, Weihong

Abstract

The common issue of surrogate models is to make good use of sampling data. In theory, the higher the fidelity of sampling data provided, the more accurate the approximation model built. However, in practical engineering problems, high-fidelity data may be less available, and such data may also be computationally expensive. On the contrary, we often obtain low-fidelity data under certain simplifications. Although low-fidelity data is less accurate, such data still contains much information about the real system. So, combining both high and low multi-fidelity data in the construction of a surrogate model may lead to better representation of the physical phenomena. Co-Kriging is a method based on a two-level multi-fidelity data. In this work, a Co-Kriging method which expands the usual two-level to multi-level multi-fidelity is proposed to improve the approximation accuracy. In order to generate the different fidelity data, the POD model reduction is used with varying number of the basis vectors. Three numerical examples are tested to illustrate not only the feasibility and effectiveness of the proposed method but also the better accuracy when compared with Kriging and classical Co-Kriging.

Suggested Citation

  • Xiao, Manyu & Zhang, Guohua & Breitkopf, Piotr & Villon, Pierre & Zhang, Weihong, 2018. "Extended Co-Kriging interpolation method based on multi-fidelity data," Applied Mathematics and Computation, Elsevier, vol. 323(C), pages 120-131.
    • Handle: RePEc:eee:apmaco:v:323:y:2018:i:c:p:120-131
    DOI: 10.1016/j.amc.2017.10.055
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300317307646
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.amc.2017.10.055?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Prashant Singh & Ivo Couckuyt & Khairy Elsayed & Dirk Deschrijver & Tom Dhaene, 2017. "Multi-objective Geometry Optimization of a Gas Cyclone Using Triple-Fidelity Co-Kriging Surrogate Models," Journal of Optimization Theory and Applications, Springer, vol. 175(1), pages 172-193, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Benyu Li & Deyun Zhong & Liguan Wang, 2021. "Repair of Geological Models Based on Multiple Material Marching Cubes," Mathematics, MDPI, vol. 9(18), pages 1-17, September.
    2. Dawei Zhan & Huanlai Xing, 2020. "Expected improvement for expensive optimization: a review," Journal of Global Optimization, Springer, vol. 78(3), pages 507-544, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Haddad, Hassan Z. & Mohamed, Mohamed H. & Shabana, Yasser M. & Elsayed, Khairy, 2023. "Optimization of Savonius wind turbine with additional blades by surrogate model using artificial neural networks," Energy, Elsevier, vol. 270(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:apmaco:v:323:y:2018:i:c:p:120-131. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/applied-mathematics-and-computation .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.