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...
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| Principais autores: | , , , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Wiley
2026-01-01
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| coleção: | International Journal of Mathematics and Mathematical Sciences |
| Acesso em linha: | http://dx.doi.org/10.1155/ijmm/6147880 |
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