Gaussian Processes with Derivatives (GPD) model has been widely used as a surrogate model in engineering to mitigate computational costs. However, most existing GPD models do not consider multi-fidelity derivatives and the relationship between high-fidelity (HF) and low-fidelity (LF) models. In this paper, we propose a novel GPD model called multi-fidelity Gaussian processes with derivatives (MFGPD), in which the posterior mean of the LF GPD replaces the prior mean of the HF GPD. The relationship between the HF and LF models is depicted by an unknown function. To demonstrate the performance of MFGPD, we provide two analytical examples and an aerodynamic coefficient modeling case, as well as comparisons to two other surrogate models. Results show that MFGPD outperforms the other models in both global and local prediction, indicating great potential for engineering applications.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Multi-fidelity Gaussian Processes with Derivatives for Aerodynamic Data Fusion


    Contributors:
    Jia, Jia-Xing (author) / Lian, Feng (author) / Fan, Zi-En (author)


    Publication date :

    2023-07-18


    Size :

    887647 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Multi-Fidelity for MDO Using Gaussian Processes

    Garland, Nicolas / Le Riche, Rodolphe / Richet, Yann et al. | Springer Verlag | 2020




    MULTI-FIDELITY GAUSSIAN PROCESS MODEL FOR CFD AND WIND TUNNEL DATA FUSION

    Conde-Arenzana, R. / López-Lopera, A. F. / Mouton, S. et al. | TIBKAT | 2021

    Free access

    DIMENSIONALITY REDUCTION FOR MULTI-FIDELITY GAUSSIAN PROCESSES USING BAYESIAN ADAPTATION

    Tsilifis, Panagiotis / Pandita, Piyush / Ghosh, Sayan et al. | TIBKAT | 2021