Accession Number:

AD1076676

Title:

An Efficient Batch K-Fold Cross-Validation Voronoi Adaptive Sampling Technique for Global Surrogate Modeling

Descriptive Note:

Technical Report

Corporate Author:

CFD Research Corporation Huntsville United States

Report Date:

2019-05-01

Pagination or Media Count:

22.0

Abstract:

Surrogate models can be used to approximate complex systems at a reduced cost and are widely used when data generation is expensive or time consuming. The accuracy of these models is dependent on the samples used to create them. Therefore, proper selection of samples within the parameter space is of paramount importance. Adaptive sampling procedures have been developed to identify optimal locations for new samples by leveraging response information from existing samples. In this manner, adaptive sampling methods significantly reduce the number of samples required to build a surrogate model of a given accuracy. However, adaptive sampling techniques have a cost associated with determining ideal sample locations, which typically grows with the sample count. The present effort seeks to reduce the cost associated with the adaptive sampling procedure and thereby maximize the efficiency of surrogate model creation. A new K-fold cross-validation KFCV-Voronoi adaptive sampling technique is proposed to reduce the sample selection costs by adding a global KFCV filter to the cross-validation CV-Voronoi technique. The costs are further reduced through an innovative Voronoi batch sampling technique. The proposed adaptive sampling acceleration techniques are evaluated using benchmark functions with increasing parameter space dimension and aerodynamic loading data.

Subject Categories:

  • Information Science
  • Numerical Mathematics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE