Adaptive infill sampling strategy for metamodeling: Challenge and future research directions
The widespread use of computer experiments for design optimization has made the issue of reducing computational cost, improving accuracy, removing the “curse of dimensionality” and avoiding expensive function approximation becoming even more important. Metamodeling also known as surrogate modeling,...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Institute of Advanced Engineering
2020
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/91183/1/ShahrumShahAbdullah2020_AdaptiveInfillSamplingStrategyforMetamodeling.pdf http://eprints.utm.my/id/eprint/91183/ http://dx.doi.org/10.11591/eei.v9i5.2162 |
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| Summary: | The widespread use of computer experiments for design optimization has made the issue of reducing computational cost, improving accuracy, removing the “curse of dimensionality” and avoiding expensive function approximation becoming even more important. Metamodeling also known as surrogate modeling, can approximate the actual simulation model allowing for much faster execution time thus becoming a useful method to mitigate these problems. There are two (2) well-known metamodeling techniques which is kriging and radial basis function (RBF) discussed in this paper based on widely used algorithm tool from previous work in modern engineering design of optimization. An integral part of metamodeling is in the method to sample new data from the actual simulation model. Sampling new data for metamodeling requires finding the location (or value) of one or more new data such that the accuracy of the metamodel can be increased as much as possible after the sampling process. This paper discussed the challenges of adaptive sampling in metamodel and proposed an ensemble non-homogeneous method for best model voting to obtain new sample points. |
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