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

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第一作者： Azfarizal,Mukhtar

第一单位： Institute of Sustainable Energy, Putrajaya Campus, Universiti Tenaga Nasional, Jalan IKRAM-UNITEN, 43000 Kajang, Malaysia.;College of Engineering, Putrajaya Campus, Universiti Tenaga Nasional, Jalan IKRAM-UNITEN, 43000 Kajang, Malaysia.

作者： Azfarizal,Mukhtar ^[1] ; Ahmad Shah Hizam Md,Yasir ^[2] ; Mohamad Fariz Mohamed,Nasir ^[3]

作者单位： Institute of Sustainable Energy, Putrajaya Campus, Universiti Tenaga Nasional, Jalan IKRAM-UNITEN, 43000 Kajang, Malaysia.;College of Engineering, Putrajaya Campus, Universiti Tenaga Nasional, Jalan IKRAM-UNITEN, 43000 Kajang, Malaysia. ^[1] Faculty of Resilince, Rabdan Academy, 65, Al Inshirah, Al Sa'adah, 22401, PO Box: 114646, Abu Dhabi, United Arab Emirates. ^[2] STARE Resources Sdn. Bhd., Wisma Rampai, 2-4-29, Fourth Floor, Jalan 34/26, Taman Sri Rampai 53300 Wilayah Persekutuan Kuala Lumpur, Malaysia. ^[3]

关键词 -Surrogate model Design of experiment (DOE)Kriging-based model Polynomial regression (PR)Underground shelter

DOI 10.1016/j.heliyon.2023.e18674

PMID 37554836

发布时间 2023-08-10

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

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

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