Ensemble of surrogates and cross-validation for rapid and accurate predictions using small data sets. Issue 4 (18th November 2019)

• Record Type: Journal Article
• Authors: Alizadeh, Reza; Jia, Liangyue; Nellippallil, Anand Balu; Wang, Guoxin; Hao, Jia; Allen, Janet K.; Mistree, Farrokh
• Editors: Biswas, Pradipta; Orero, Pilar; Sezgin, Metin
• Is Part Of: AI EDAM. Volume 33:Issue 4(2019)
• Page Start: 484
• Page End: 501
• Publication Date: 2019-11-18
• Subjects: Ensemble of surrogates, -- kriging, -- response surface modeling, -- small data sets, -- surrogate models; Engineering design -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; 620.00420285
• DOI: 10.1017/S089006041900026X
• Languages: English
• ISSNs: 0890-0604

Abstract

Abstract: In engineering design, surrogate models are often used instead of costly computer simulations. Typically, a single surrogate model is selected based on the previous experience. We observe, based on an analysis of the published literature, that fitting an ensemble of surrogates (EoS) based on cross-validation errors is more accurate but requires more computational time. In this paper, we propose a method to build an EoS that is both accurate and less computationally expensive. In the proposed method, the EoS is a weighted average surrogate of response surface models, kriging, and radial basis functions based on overall cross-validation error. We demonstrate that created EoS is accurate than individual surrogates even when fewer data points are used, so computationally efficient with relatively insensitive predictions. We demonstrate the use of an EoS using hot rod rolling as an example. Finally, we include a rule-based template which can be used for other problems with similar requirements, for example, the computational time, required accuracy, and the size of the data.