Dimensionality Reduction in Surrogate Modeling: A Review of Combined Methods

Surrogate modeling has been popularized as an alternative to full-scale models in complex engineering processes such as manufacturing and computer-assisted engineering. The modeling demand exponentially increases with complexity and number of system parameters, which consequently requires higher-dim...

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Vydané v:	Data Science and Engineering Ročník 7; číslo 4; s. 402 - 427
Hlavní autori:	Hou, Chun Kit Jeffery, Behdinan, Kamran
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Singapore Springer Nature Singapore 01.12.2022 Springer Springer Nature B.V
Predmet:	Accuracy Algorithm Analysis and Problem Complexity Algorithms Artificial Intelligence Chemistry and Earth Sciences Complexity Computer Science Data mining Data Mining and Knowledge Discovery Database Management Datasets Discriminant analysis Efficiency Eigenvalues Eigenvectors Engineering Feature extraction Feature selection Investigations Linear algebra Literature reviews Machine learning Nonlinear phenomena Physics Principal components analysis Reduction Review/Survey Papers Scale models State-of-the-art reviews Statistics for Engineering Systems and Data Security Canada Dimensionality reduction High dimensionality Surrogate modeling Neural networks Machine learning
ISSN:	2364-1185, 2364-1541, 2364-1541
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