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Global Surrogate Modeling by Neural Network-Based Model Uncertainty

Abstract

This work proposes a novel adaptive global surrogate modeling algorithm which uses two neural networks, one for prediction and the other for the model uncertainty. Specifically, the algorithm proceeds in cycles and adaptively enhances the neural network-based surrogate model by selecting the next sampling points guided by an auxiliary neural network approximation of the spatial error. The proposed algorithm is tested numerically on the one-dimensional Forrester function and the two-dimensional Branin function. The results demonstrate that global surrogate modeling using neural network-based function prediction can be guided efficiently and adaptively using a neural network approximation of the model uncertainty.

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Details

Category:
Conference activity
Type:
publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
Language:
English
Publication year:
2022
Bibliographic description:
Leifsson L., Nagawkar J., Barnet L., Bryden K., Kozieł S., Pietrenko-Dąbrowska A.: Global Surrogate Modeling by Neural Network-Based Model Uncertainty// / : , 2022,
DOI:
Digital Object Identifier (open in new tab) 10.1007/978-3-031-08757-8_35
Sources of funding:
  • COST_FREE
Verified by:
Gdańsk University of Technology

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