典型文献
Selecting scale factor of Bayesian multi-fidelity surrogate by minimizing posterior variance
文献摘要：
The Bayesian Multi-Fidelity Surrogate(MFS)proposed by Kennedy and O'Hagan(KOH model)has been widely employed in engineering design,which builds the approximation by decomposing the high-fidelity function into a scaled low-fidelity model plus a discrepancy func-tion.The scale factor before the low-fidelity function,p,plays a crucial role in the KOH model.This scale factor is always tuned by the Maximum Likelihood Estimation(MLE).However,recent stud-ies reported that the MLE may sometimes result in MFS of bad accuracy.In this paper,we first present a detailed analysis of why MLE sometimes can lead to MFS of bad accuracy.This is because,the MLE overly emphasizes the variation of discrepancy function but ignores the function waviness when selecting p.To address the above issue,we propose an alternative approach that chooses p by minimizing the posterior variance of the discrepancy function.Through tests on a one-dimensional function,two high-dimensional functions,and a turbine blade design problem,the proposed approach shows better accuracy than or comparable accuracy to MLE,and the pro-posed approach is more robust than MLE.Additionally,through a comparative test on the design optimization of a turbine endwall cooling layout,the advantage of the proposed approach is further validated.
文献关键词：
中图分类号：
作者姓名：
Hongyan BU;Liming SONG;Zhendong GUO;Jun LI
作者机构：
Institute of Turbomachinery,School of Energy&Power Engineering,Xi'an Jiaotong University,Xi'an 710049,China
文献出处：
引用格式：
[1]Hongyan BU;Liming SONG;Zhendong GUO;Jun LI-.Selecting scale factor of Bayesian multi-fidelity surrogate by minimizing posterior variance)[J].中国航空学报（英文版）,2022(11):59-73
A类：
Hagan,endwall
B类：
Selecting,Bayesian,multi,fidelity,surrogate,by,minimizing,posterior,variance,Multi,Fidelity,Surrogate,MFS,proposed,Kennedy,KOH,model,been,widely,employed,engineering,design,which,builds,approximation,decomposing,high,into,scaled,low,plus,discrepancy,before,plays,crucial,role,This,always,tuned,Maximum,Likelihood,Estimation,MLE,However,recent,stud,ies,reported,that,may,sometimes,result,bad,accuracy,In,this,paper,first,present,detailed,analysis,why,can,lead,because,overly,emphasizes,variation,but,ignores,waviness,when,selecting,To,address,above,issue,alternative,approach,chooses,Through,tests,one,dimensional,two,functions,turbine,blade,problem,shows,better,than,comparable,more,robust,Additionally,through,comparative,optimization,cooling,layout,advantage,further,validated
AB值：
0.562425
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