In this paper, we propose the use of the composite kernel function (CKL) technique for the multi-fidelity Kriging (MFK) surrogate model. The principle of MFK-CKL is to automatically learn the best combination of single kernels in both low- and high-fidelity Kriging to create a more accurate Kriging model. The combination is in the form of a weighted sum in which the weights are treated as extra hyperparameters. We implement the CKL into the recursive co-Kriging approach. It is relatively straightforward to do the same for other MFK approaches. Demonstrations on a set of non-algebraic problems show the high efficacy of MFK with CKL, outperforming the single kernel approach in terms of approximation error. Besides, the use of CKL successfully eliminates the non-trivial process of manual kernel selection in multi-fidelity Kriging.

Composite Kernel Functions for Surrogate Modeling using Recursive Multi-Fidelity Kriging

Lucia Parussini;Luigi Bregant;
2022

Abstract

In this paper, we propose the use of the composite kernel function (CKL) technique for the multi-fidelity Kriging (MFK) surrogate model. The principle of MFK-CKL is to automatically learn the best combination of single kernels in both low- and high-fidelity Kriging to create a more accurate Kriging model. The combination is in the form of a weighted sum in which the weights are treated as extra hyperparameters. We implement the CKL into the recursive co-Kriging approach. It is relatively straightforward to do the same for other MFK approaches. Demonstrations on a set of non-algebraic problems show the high efficacy of MFK with CKL, outperforming the single kernel approach in terms of approximation error. Besides, the use of CKL successfully eliminates the non-trivial process of manual kernel selection in multi-fidelity Kriging.
9781624106316
https://arc.aiaa.org/doi/10.2514/6.2022-0506
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11368/3025686
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