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

Published online by Cambridge University Press:  18 October 2019

Reza Alizadeh ,
Liangyue Jia ,
Anand Balu Nellippallil ,
Guoxin Wang Open the ORCID record for Guoxin Wang [Opens in a new window] ,
Jia Hao ,
Janet K. Allen  and
Farrokh Mistree
Reza Alizadeh
Affiliation:
Systems Realization Laboratory, University of Oklahoma, Norman, OK, USA
Liangyue Jia
Affiliation:
School of Mechanical Engineering, Institute for Industrial Engineering, Beijing Institute of Technology, Beijing, China
Anand Balu Nellippallil
Affiliation:
Center for Advanced Vehicular Systems, School of Mechanical Engineering, Mississippi State University, Starkville, MS, USA
Guoxin Wang*
Affiliation:
School of Mechanical Engineering, Institute for Industrial Engineering, Beijing Institute of Technology, Beijing, China
Jia Hao
Affiliation:
School of Mechanical Engineering, Institute for Industrial Engineering, Beijing Institute of Technology, Beijing, China
Janet K. Allen
Affiliation:
Systems Realization Laboratory, University of Oklahoma, Norman, OK, USA
Farrokh Mistree
Affiliation:
Systems Realization Laboratory, University of Oklahoma, Norman, OK, USA
*
Author for correspondence: Guoxin Wang, E-mail: wangguoxin@bit.edu.cn
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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.

Keywords

Ensemble of surrogateskrigingresponse surface modelingsmall data setssurrogate models
Type
Research Article
Information
AI EDAM , Volume 33 , Special Issue 4: Intelligent Interaction Design , November 2019 , pp. 484 - 501
Copyright
Copyright © Cambridge University Press 2019

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