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• W2019389302 abstract "A unified framework for surrogate model training point selection and error estimation is proposed. Building auxiliary local surrogate models over subdomains of the global surrogate model forms the basis of the proposed framework. A discrepancy function, defined as the absolute difference between response predictions from local and global surrogate models for randomly chosen test candidates, drives the framework, thereby not requiring any additional exact function evaluations. The benefits of this new approach are demonstrated with analytical test functions and the construction of a two-dimensional aerodynamic database. The results show that the proposed training point selection approach improves the convergence monotonicity and produces more accurate surrogate models compared to random and quasi-random training point selection strategies. The introduced root-mean-square discrepancy and maximum absolute discrepancy exhibit close agreement with the actual root-mean-square error and maximum absolute error, r..." @default.
• W2019389302 created "2016-06-24" @default.
• W2019389302 creator A5054806727 @default.
• W2019389302 creator A5087704644 @default.
• W2019389302 date "2015-01-01" @default.
• W2019389302 modified "2023-09-26" @default.
• W2019389302 title "Unified Framework for Training Point Selection and Error Estimation for Surrogate Models" @default.
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• W2019389302 doi "https://doi.org/10.2514/1.j053064" @default.
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