Abstract
The analysis of complex engineering systems can often be expensive thereby necessitating the use of surrogate models within any design optimization. However, the time variant response of quantities of interest can be non-stationary in nature and therefore difficult to represent effectively with traditional surrogate modelling techniques. The following paper presents the application of partial non-stationary kriging to the prediction of time variant responses where the definition of the non-linear mapping scheme is based upon prior knowledge of either the inputs to, or the nature of, the engineering system considered.
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© 2011 Springer-Verlag Berlin Heidelberg
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Toal, D.J.J., Keane, A.J. (2011). Towards an Intelligent Non-stationary Performance Prediction of Engineering Systems. In: Coello, C.A.C. (eds) Learning and Intelligent Optimization. LION 2011. Lecture Notes in Computer Science, vol 6683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25566-3_33
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DOI: https://doi.org/10.1007/978-3-642-25566-3_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25565-6
Online ISBN: 978-3-642-25566-3
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