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• W2342965561 abstract "Over the last decade, surrogate modelling technique has gained wide popularity in the field of uncertainty quantification, optimization, model exploration and sensitivity analysis. This approach relies on experimental design to generate training points and regression/interpolation for generating the surrogate. In this work, it is argued that conventional experimental design may render a surrogate model inefficient. In order to address this issue, this paper presents a novel distribution adaptive sequential experimental design (DA-SED). The proposed DA-SED has been coupled with a variant of generalised analysis of variance (G-ANOVA), developed by representing the component function using the generalised polynomial chaos expansion. Moreover, generalised analytical expressions for calculating the first two statistical moments of the response, which are utilized in predicting the probability of failure, have also been developed. The proposed approach has been utilized in predicting probability of failure of three structural mechanics problems. It is observed that the proposed approach yields accurate and computationally efficient estimate of the failure probability." @default.
• W2342965561 created "2016-06-24" @default.
• W2342965561 creator A5030664714 @default.
• W2342965561 creator A5042947686 @default.
• W2342965561 date "2016-07-01" @default.
• W2342965561 modified "2023-10-18" @default.
• W2342965561 title "Sequential experimental design based generalised ANOVA" @default.
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• W2342965561 doi "https://doi.org/10.1016/j.jcp.2016.04.042" @default.
• W2342965561 hasPublicationYear "2016" @default.
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