Abstract
Full lifecycle high fidelity digital twin is a complex model set contains multiple functions with high dimensions and multiple variables.Quantifying uncertainty for such complex models often encounters time-consuming challenges,as the number of calculated terms increases exponen-tially with the dimensionality of the input.This paper based on the multi-stage model and high time consumption problem of digital twins,proposed a sparse polynomial chaos expansions method to generate the digital twin dynamic-polymorphic uncertainty surrogate model,striving to strike a bal-ance between the accuracy and time consumption of models used for digital twin uncertainty quan-tification.Firstly,an analysis and clarification were conducted on the dynamic-polymorphic uncertainty of the full lifetime running digital twins.Secondly,a sparse polynomial chaos expan-sions model response was developed based on partial least squares technology with the effectively quantified and selected basis polynomials which sorted by significant influence.In the end,the accu-racy of the proxy model is evaluated by leave-one-out cross-validation.The effectiveness of this method was verified through examples,and the results showed that it achieved a balance between maintaining model accuracy and complexity.
维普
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