... 30 05 2023 approximation-based optimal design metamodeling National Natural Science Foundation of China 10.13039/501100001809 Grant No. 51575205 Accepted Manuscript Not Copyedited Journal of Mechanical Design. Received November 21, 2022; Accepted manuscript posted May 22...

... requires few calls to the functions but is not appropriate for nonlinear functions. multidisciplinary analysis derivative-based global sensitivity measures multifidelity kriging metamodeling multidisciplinary design and optimization sensitivity analysis for design 02 07 2022 01 02...

... in the J ournal of M echanical D esign . 04 01 2023 09 03 2023 10 03 2023 16 05 2023 approximation-based optimal design design optimization Machine learning metamodeling multiobjective optimization Accepted Manuscript Not Copyedited Journal of Mechanical Design...

... Contributed by the Design Automation Committee of ASME for publication in the J ournal of M echanical D esign . 02 04 2022 30 01 2023 02 02 2023 15 03 2023 approximation-based optimal design design of experiments metamodeling space-filling design Division...

... author. Email: rmalak@tamu.edu Contributed by the Design Automation Committee of ASME for publication in the J ournal of M echanical D esign . 15 06 2022 20 01 2023 23 01 2023 17 02 2023 design optimization metamodeling multi-objective optimization Bayesian...

... in the J ournal of M echanical D esign . 03 07 2022 14 10 2022 29 10 2022 24 11 2022 ensemble remaining useful life lithium-ion batteries rolling element bearings machine learning metamodeling Accurately predicting the remaining useful life (RUL) of equipment...

.... There are no conflicts of interest. The datasets generated and supporting the findings of this article are obtainable from the corresponding author upon reasonable request. References [1] Oune , N. , and Bostanabad , R. , 2021 , “ Latent Map Gaussian Processes for Mixed Variable Metamodeling...

... J. Mech. Des. , 135 ( 1 ), p. 011009 . 10.1115/1.4022999 [31] Li , M. , Li , G. , and Azarm , S. , 2008 , “ A Kriging Metamodel Assisted Multi-objective Genetic Algorithm for Design Optimization ,” ASME J. Mech. Des. , 130 ( 3 ), p. 031401 . 10.1115/1.2829879 [32...

... Optimization for Design Under Uncertainty Problems Utilizing Kriging Metamodeling in Augmented Input Space ,” Comput. Methods Appl. Mech. Eng. , 315 , pp. 369 – 395 . 10.1016/j.cma.2016.10.042 [23] Gao , T. , and Li , J. , 2017 , “ A Derivative-Free Trust-Region Algorithm for Reliability...

... that takes advantages of both empirical and FE models. The validity and accuracy of the metamodel developed in the earlier framework depend on the initial assumption of parameter uncertainties. This causes a problem when the assumed uncertainties are far from the actual values. The proposed framework...

... Automation Committee of ASME for publication in the J ournal of M echanical D esign . 20 02 2022 24 06 2022 29 06 2022 22 07 2022 robust design optimization multi-fidelity surrogate sequential sampling metamodeling National Natural Science Foundation of China...

... learning metamodeling reliability in design uncertainty analysis National Natural Science Foundation of China 10.13039/501100001809 51875525 51905475 52111540267 Natural Science Foundation of Zhejiang Province 10.13039/501100004731 LY21E050008 Structural reliability...

... echanical D esign . 01 12 2021 28 04 2022 04 05 2022 13 06 2022 Gaussian processes data fusion calibration emulation manifold learning machine learning metamodeling uncertainty modeling National Aeronautics and Space Administration 10.13039/100000104...

... learning metamodeling uncertainty modeling Design of experiments (DOE) [ 1 , 2 ] is an essential aspect of most, if not all, engineering tasks. Tasks ranging from choosing geometric parameters for aircraft rotors to pharmacological drug design [ 3 ] require careful experimentation. An optimally...

... Surface Metamodels and Optimal Design of Experiments for Gravity Dams ,” Struct. Infrastruct. Eng. , 14 ( 12 ), pp. 1641 – 1663 . 10.1080/15732479.2018.1469650 [5] Chatterjee , T. , Chakraborty , S. , and Chowdhury , R. , 2019 , “ A Critical Review of Surrogate Assisted Robust...

... to approximate high-dimensional problems in engineering design. However, the available one-metamodel-based HDMRs usually encounter the predicament of prediction uncertainty, while current multi-metamodels-based HDMRs cannot provide simple explicit expressions for black-box problems, and have high computational...

... types noisy data regression approximate interpolation metamodeling Recent developments in data analysis and approximation methods have aroused great interest in surrogate models, also known as response surface models, meta-models, or approximation models. Such models have been embraced...

... Occupant Protection System Based on Ensemble of Metamodels ,” Struct. Multidiscipl. Optim. , 51 ( 2 ), pp. 533 – 546 . 10.1007/s00158-014-1150-7 [8] Liu , Q. , Wu , X. , Han , X. , Liu , J. , Zhang , Z. , and Guo , S. , 2020 , “ Sensitivity Analysis and Interval Multi...

... 15 10 2021 19 10 2021 25 11 2021 artificial intelligence data-driven design metamodeling simulation-based design systems design Defense Advanced Research Projects Agency 10.13039/100000185 HR00112090069 Mission engineering and design (ME&D...

... . 10.1109/TEVC.2009.2033671 [8] Li , M. , Li , G. , and Azarm , S. , 2008 , “ A Kriging Metamodel Assisted Multi-Objective Genetic Algorithm for Design Optimization ,” J. Mech. Des. , 130 ( 3 ), p. 031401 . 10.1115/1.2829879 [9] Li , G. , Li , M. , Azarm , S...