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| A sequential sampling method of surrogate model based on k-fold cross validation |
| Received:November 10, 2020 Revised:March 09, 2021 |
| View Full Text View/Add Comment Download reader |
| DOI：10.7511/jslx20201110001 |
| KeyWord:k-fold cross validation sequential sample surrogate model Voronoi diagram |
| Author | Institution |
| 李正良 |
重庆大学 土木工程学院, 重庆 ;重庆大学 山地城镇建设与新技术教育部重点实验室, 重庆 |
| 彭思思 |
重庆大学 土木工程学院, 重庆 |
| 王涛 |
重庆大学 土木工程学院, 重庆 |
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| Abstract: |
| Under the framework of sequence sampling of surrogate models,in view of the shortcomings of existing methods,a k-fold CV-Voronoi adaptive sequential sampling method was developed,which is suitable for arbitrary surrogate models.In this method,the k-fold cross-validation was introduced to calculate the prediction error of sample points,and the Voronoi diagram and maxmin criterion were combined.Compared with the traditional sequential sampling method,the proposed method has the advantages of calculation simplicity and strong adaptability.Through the numerical examples and engineering example,it is shown that the proposed sequential sampling method has high accuracy and calculation efficiency.In addition,the influence of different k values on the accuracy of surrogate model is further discussed in the k-fold cross-validation,and the optimal range of k values is summarized for reference. |
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