A multi-fidelity surrogate modelling approach for shape optimization, which relies on adaptive techniques to obtain good performance for a large range of problems, is presented and critically evaluated. Furthermore, an approach to adaptive selection of the fidelity levels to be used is presented. Adaptation is shown to be effective for solving complex problems. Finally, potential improvements in the noise canceling, the uncertainty estimation, and the adaptive sampling are identified.
Adaptive Multi-fidelity Surrogate Modelling for High-quality Shape Optimization
R Pellegrini;A Serani;M Diez
2022
Abstract
A multi-fidelity surrogate modelling approach for shape optimization, which relies on adaptive techniques to obtain good performance for a large range of problems, is presented and critically evaluated. Furthermore, an approach to adaptive selection of the fidelity levels to be used is presented. Adaptation is shown to be effective for solving complex problems. Finally, potential improvements in the noise canceling, the uncertainty estimation, and the adaptive sampling are identified.File in questo prodotto:
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Descrizione: Adaptive Multi-fidelity Surrogate Modelling for High-quality Shape Optimization
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