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Abstract
Uncertainty quantification (UQ) of an engineered system involves the identification of uncertainties, modeling of the uncertainties, and the forward propagation of the uncertainties through a system analysis model. In this work, a novel surrogate-based forward propagation algorithm for UQ is proposed. The proposed algorithm is a new and unique extension of the recent efficient global optimization using neural network (NN)-based prediction and uncertainty (EGONN) algorithm which was created for optimization. The proposed extended algorithm is specifically created for UQ and is called uqEGONN. The uqEGONN algorithm sequentially and simultaneously samples two NNs, one for the prediction of a nonlinear function and the other for the prediction uncertainty. The uqEGONN algorithm terminates based on the absolute relative changes in the summary statistics based on Monte Carlo simulations (MCS), or a given maximum number of sequential samples. The algorithm is demonstrated on the UQ of the Ishigami function. The results show that the proposed algorithm yields comparable results as MCS on the true function and those results are more accurate than the results obtained using space-filling Latin hypercube sampling to train the NNs.
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Authors (4)
-
Pavankumar Koratikere
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Leifur Leifsson
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Keywords
Details
- Category:
- Conference activity
- Type:
- publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
- Language:
- English
- Publication year:
- 2023
- Bibliographic description:
- Koratikere P., Leifsson L., Kozieł S., Pietrenko-Dąbrowska A.: Efficient uncertainty quantification using sequential sampling-based neural networks// / : , 2023,
- DOI:
- Digital Object Identifier (open in new tab) 10.1007/978-3-031-36024-4_41
- Sources of funding:
-
- Free publication
- Verified by:
- Gdańsk University of Technology
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