A machine learning-based comparative analysis of surrogate models for design optimisation in computational fluid dynamics

• 主要な著者: Mukhtar A., Yasir A.S.H.M., Nasir M.F.M.
• その他の著者: 57195426549
• フォーマット: 論文
• 出版事項: Elsevier Ltd 2024
• 主題: -Surrogate model; Design of experiment (DOE); Kriging-based model; Polynomial regression (PR); Underground shelter

Complex computer codes are frequently used in engineering to generate outputs based on inputs, which can make it difficult for designers to understand the relationship between inputs and outputs and to determine the best input values. One solution to this issue is to use design of experiments (DOE)...