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- W4283278712 abstract "Surrogate modeling is an attractive strategy for engineering analyses such as uncertainty quantification (UQ) and optimization-under-uncertainty (OUU), which require extensive repeated evaluation of potentially expensive black-box computer models, since it can provide good predictions of the model output based on a limited number of full model evaluations. The accuracy of a surrogate model for a given number of samples varies depending on the sample locations and outputs, so adaptive sampling techniques have been developed to iteratively add samples based on a prediction of the capacity for a sample point to improve the quality of the model. Such methods predict the contribution of new samples based on output data acquired in previous iterations, but they traditionally neglect the availability of accurate and efficient gradient information from the black-box model. Given the proliferation of adjoint-based gradient computation for engineering models, we propose a novel adaptive in-fill method for gradient-enhanced sampling (AIGES) method, which leverages gradient information of the sample outputs to efficiently place subsequent sample points. The preliminary results for the modeling of two-dimensional problems using this method show promise for improved sampling efficiency over one-shot sampling techniques based on reduced root-mean-square error as well as improved UQ accuracy." @default.
- W4283278712 created "2022-06-23" @default.
- W4283278712 creator A5074780066 @default.
- W4283278712 creator A5088748633 @default.
- W4283278712 date "2022-06-20" @default.
- W4283278712 modified "2023-09-27" @default.
- W4283278712 title "An Adaptive Sampling Method for Gradient-Enhanced Surrogate Modeling" @default.
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- W4283278712 doi "https://doi.org/10.2514/6.2022-3998" @default.
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