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Proc. ASME. IDETC-CIE2020, Volume 11B: 46th Design Automation Conference (DAC), V11BT11A025, August 17–19, 2020
Paper No: DETC2020-22257
... on global sensitivity analysis (GSA). Sensitivities with respect to individual uncertain parameters and those that consider coupled effects are all included. We utilize Kriging-based metamodels to improve the efficiency of the GSA problems with computationally expensive simulations. The optimal design...
Proc. ASME. IDETC-CIE2010, Volume 1: 36th Design Automation Conference, Parts A and B, 599-609, August 15–18, 2010
Paper No: DETC2010-28958
... capability beyond the current second order form and “uncover” black-box functions so that not only a more accurate metamodel is obtained, but also key information of the function can be gained and thus the black-box function can be turned “white.” The key information that can be gained includes 1) functional...
Proc. ASME. IDETC-CIE2004, Volume 1: 30th Design Automation Conference, 481-492, September 28–October 2, 2004
Paper No: DETC2004-57300
... The use of kriging models for approximation and metamodel-based design and optimization has been steadily on the rise in the past decade. The widespread usage of kriging models appears to be hampered by (1) the lack of guidance in selecting the appropriate form of the kriging model, (2...
Proc. ASME. IDETC-CIE2003, Volume 2: 29th Design Automation Conference, Parts A and B, 605-614, September 2–6, 2003
Paper No: DETC2003/DAC-48766
... element computations — that are usually obtained after lengthy simulations. We propose to use metamodeling techniques (MM) to generate approximated mathematical models of these analyses which can be employed directly within a CP environment, expanding the scope of CP to applications that previously could...
Proc. ASME. IDETC-CIE2003, Volume 2: 29th Design Automation Conference, Parts A and B, 527-533, September 2–6, 2003
Paper No: DETC2003/DAC-48758
... Kriging is a popular metamodeling technique for analysis of computer experiment. However, the likelihood function near the optimum is flat in some situations, and this may lead to very large random variation in the maximum likelihood estimate. To overcome this difficulty, a penalized likelihood...