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Abstract
Performing global sensitivity analysis (GSA) can be challenging due to the combined effect of the high computational cost, but it is also essential for engineering decision making. To reduce this cost, surrogate modeling such as neural networks (NNs) are used to replace the expensive simulation model in the GSA process, which introduces the additional challenge of finding the minimum number of training data samples required to train the NNs accurately. In this work, a recently proposed NN-based GSA algorithm to accurately quantify the sensitivities is improved. The algorithm iterates over the number of samples required to train the NNs and terminates using an outer-loop sensitivity convergence criteria. The iterative surrogate-based GSA yields converged values for the Sobol’ indices and, at the same time, alleviates the specification of arbitrary accuracy metrics for the NN-based approximation model. In this paper, the algorithm is improved by enhanced NN modeling, which lead to an overall acceleration of the GSA process. The improved algorithm is tested numerically on problems involving an analytical function with three input parameters, and a simulation-based nondestructive evaluation problem with three input parameters.
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Authors (4)
-
Yen-Chen Liu
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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:
- 2022
- Bibliographic description:
- Liu Y., Leifsson L., Kozieł S., Pietrenko-Dąbrowska A.: Neural Network-Based Sequential Global Sensitivity Analysis Algorithm// / : , 2022,
- DOI:
- Digital Object Identifier (open in new tab) 10.1007/978-3-031-08757-8_37
- Sources of funding:
-
- Free publication
- Verified by:
- Gdańsk University of Technology
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