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Adaptive PC-Kriging-Based sparse reduced-Order modeling framework for complex engineering systems
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DOI：10.1016/j.rineng.2026.109328.png)
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摘要
En 中文
• A sparse HDMR framework is proposed, which builds components based on arbitrary Polynomial Chaos-Kriging(aPCK) and uses DIP to eliminate weak interaction terms, reduce computational costs, and maintain high accuracy. • Introduces Interaction-Focused Alternating Cut Sampling(IFACS) to balance global exploration and local feature capture in high-dimensional subspaces. • Validates on 30D nonlinear systems, achieving superior sample efficiency and robustness compared to KRG-HDMR, PCE-HDMR and PCK-HDMR. • The performance of the proposed method has been experimentally validated in different engineering examples including cantilever beams, composite beams, and Oldman River hydrological model.
Keyword:
Sparse HDMR
PC-Kriging
Dynamic interaction pruning
Adaptive sampling
Uncertainty quantification
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期刊
IF:
7.9
论文数:
1.2W
被引数:
1.7W
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