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• W2295970615 abstract "Computational simulation models with variable fidelity have been widely used in complex systems design. However, running the most accurate simulation models tends to be very time-consuming and can therefore only be used sporadically, while incorporating less accurate, inexpensive models into the design process may result in inaccurate design alternatives. To make a trade-off between high accuracy and low expense, variable fidelity (VF) metamodeling approaches that aim to integrate information from both low-fidelity (LF) and high-fidelity (HF) models have gained increasing popularity. In this paper, an adaptive global VF metamodeling approach named difference adaptive decreasing variable-fidelity metamodeling (DAD-VFM) is proposed, in which the one-shot VF metamodeling process is transformed into an iterative process to utilize the already-acquired information of difference characteristics between the HF and LF models. In DAD-VFM, support vector regression (SVR) is adopted to map the difference between the HF and LF models. Besides, a generalized objective-oriented sampling strategy is introduced to adaptively probe and sample more points in the interesting regions where the differences between the HF and LF models are multi-model, non-smooth and have abrupt changes. Several numerical cases and a long cylinder pressure vessel optimization design problem verify the applicability of the proposed VF metamodeling approach." @default.
• W2295970615 created "2016-06-24" @default.
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• W2295970615 date "2015-12-01" @default.
• W2295970615 modified "2023-10-18" @default.
• W2295970615 title "An adaptive global variable fidelity metamodeling strategy using a support vector regression based scaling function" @default.
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• W2295970615 doi "https://doi.org/10.1016/j.simpat.2015.08.002" @default.
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