Ensemble of surrogates and cross-validation for rapid and accurate predictions using small data sets

• Published in: AI EDAM Vol. 33; no. 4; pp. 484 - 501
• Main Authors: Alizadeh, Reza, Jia, Liangyue, Nellippallil, Anand Balu, Wang, Guoxin, Hao, Jia, Allen, Janet K., Mistree, Farrokh
• Format: Journal Article
• Language: English
• Published: Cambridge Cambridge University Press 01.11.2019
• Subjects: Accuracy; Computational efficiency; Computer simulation; Computing time; Data points; Design engineering; Efficiency; Genetic algorithms; Kriging interpolation; Neural networks; Optimization; Radial basis function; Response surface methodology; Simulation
• ISSN: 0890-0604; 1469-1760
• DOI: 10.1017/S089006041900026X

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.