| Autor: |
Kerleguer, Baptiste, Cannamela, Claire, Garnier, Josselin |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
International Journal for Uncertainty Quantification, 2024, 14 (1), pp.43-60 |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1615/Int.J.UncertaintyQuantification.2023044584 |
| Popis: |
This paper deals with surrogate modelling of a computer code output in a hierarchical multi-fidelity context, i.e., when the output can be evaluated at different levels of accuracy and computational cost. Using observations of the output at low- and high-fidelity levels, we propose a method that combines Gaussian process (GP) regression and Bayesian neural network (BNN), in a method called GPBNN. The low-fidelity output is treated as a single-fidelity code using classical GP regression. The high-fidelity output is approximated by a BNN that incorporates, in addition to the high-fidelity observations, well-chosen realisations of the low-fidelity output emulator. The predictive uncertainty of the final surrogate model is then quantified by a complete characterisation of the uncertainties of the different models and their interaction. GPBNN is compared with most of the multi-fidelity regression methods allowing to quantify the prediction uncertainty. |
| Databáze: |
arXiv |
| Externí odkaz: |
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