Exploiting Supervised Principal Component Analysis for Efficient High-Dimensional Kriging Modeling
High-dimensional Kriging modeling is widely used in engineering and scientific fields. However, as the input dimensionality increases, computational complexity rises dramatically, and the number of required training samples grows exponentially. To address these challenges, this paper proposes a nove...
保存先:
| 主要な著者: | , , , , |
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| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
Wiley
2026-01-01
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| シリーズ: | International Journal of Mathematics and Mathematical Sciences |
| オンライン･アクセス: | http://dx.doi.org/10.1155/ijmm/6147880 |
| タグ: |
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