Abstract
This chapter outlines the techniques for incorporating variable-fidelity simulation models into performance-driven modeling frameworks. The presented methods of combining the data of different fidelities include space mapping, co-kriging, as well as two-level Gaussian process regression. Utilization of variable-fidelity computational models enables further reduction of the surrogate model construction cost compared to single-fidelity constrained surrogates. The main focus is on potential computational benefits that can be achieved through the involvement of low-fidelity models, as well as practical issues one needs to take into account while working within variable-fidelity setups. Our considerations are illustrated using several examples of high-frequency structures.
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Koziel, S., Pietrenko-Dabrowska, A. (2020). Variable-Fidelity Performance-Driven Modeling. In: Performance-Driven Surrogate Modeling of High-Frequency Structures. Springer, Cham. https://doi.org/10.1007/978-3-030-38926-0_9
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DOI: https://doi.org/10.1007/978-3-030-38926-0_9
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