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典型文献
Modified Multifidelity Surrogate Model Based on Radial Basis Function with Adaptive Scale Factor
文献摘要:
Multifidelity surrogates(MFSs)replace computationally intensive models by synergistically combining information from different fidelity data with a significant improvement in modeling efficiency.In this paper,a modified MFS(MMFS)model based on a radial basis function(RBF)is proposed,in which two fidelities of information can be ana-lyzed by adaptively obtaining the scale factor.In the MMFS,an RBF was employed to establish the low-fidelity model.The correlation matrix of the high-fidelity samples and corresponding low-fidelity responses were integrated into an expansion matrix to determine the scaling function parameters.The shape parameters of the basis function were optimized by minimizing the leave-one-out cross-validation error of the high-fidelity sample points.The performance of the MMFS was compared with those of other MFS models(MFS-RBF and cooperative RBF)and single-fidelity RBF using four benchmark test functions,by which the impacts of different high-fidelity sample sizes on the prediction accuracy were also analyzed.The sensitivity of the MMFS model to the randomness of the design of experiments(DoE)was investigated by repeating sampling plans with 20 different DoEs.Stress analysis of the steel plate is pre-sented to highlight the prediction ability of the proposed MMFS model.This research proposes a new multifidelity modeling method that can fully use two fidelity sample sets,rapidly calculate model parameters,and exhibit good prediction accuracy and robustness.
文献关键词:
作者姓名:
Yin Liu;Shuo Wang;Qi Zhou;Liye Lv;Wei Sun;Xueguan Song
作者机构:
School of Mechanical Engineering,Dalian University of Technology,Dalian 116000,China;School of Aerospace Engineering,Huazhong University of Science and Technology,Wuhan 430000,China;School of Mechanical Engineering and Automation,Zhejiang Sci-Tech University,Hangzhou 310000,China
引用格式:
[1]Yin Liu;Shuo Wang;Qi Zhou;Liye Lv;Wei Sun;Xueguan Song-.Modified Multifidelity Surrogate Model Based on Radial Basis Function with Adaptive Scale Factor)[J].中国机械工程学报,2022(04):254-268
A类:
Multifidelity,MFSs,MMFS,DoEs,multifidelity
B类:
Modified,Surrogate,Model,Based,Radial,Basis,Function,Adaptive,Scale,Factor,surrogates,replace,computationally,intensive,models,by,synergistically,combining,information,from,different,data,significant,improvement,modeling,efficiency,In,this,paper,modified,radial,basis,RBF,proposed,which,two,fidelities,adaptively,obtaining,scale,was,employed,establish,low,correlation,matrix,samples,corresponding,responses,were,integrated,into,expansion,determine,scaling,parameters,shape,optimized,minimizing,leave,one,out,cross,validation,error,points,performance,compared,those,other,cooperative,single,using,four,benchmark,test,functions,impacts,sizes,prediction,accuracy,also,analyzed,sensitivity,randomness,design,experiments,investigated,repeating,sampling,plans,Stress,analysis,steel,plate,sented,highlight,ability,This,research,proposes,new,method,that,fully,use,sets,rapidly,calculate,exhibit,good,robustness
AB值:
0.510885
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