... With the Acquisition Function for Convergence and Diversity ,” ASME J. Mech. Des. , 142 ( 9 ), p. 091703 . 10.1115/1.4046508 [25] Simpson , T. , Poplinski , J. , Koch , P. N. , and Allen , J. , 2001 , “ Metamodels for Computer-Based Engineering Design: Survey and Recommendations ,” Eng...

... Corresponding author. Email: sxg@dlut.edu.cn 24 02 2023 08 07 2023 10 07 2023 25 08 2023 pointwise ensemble of surrogate models model selection cross-validation pointwise weight calculation agent-based design design optimization metamodeling structural optimization...

.... Email: sxg@dlut.edu.cn Email: laixiaonan0910@163.com Email: lpw12104059@mail.dlut.edu.cn Email: shuaizhang@mail.dlut.edu.cn Email: libertyljj@mail.dlut.edu.cn ensemble of surrogate models adaptive weight strategy median metamodeling References [1] Han...

... optimization multi-objective optimization Gaussian process acquisition function data-driven design design optimization machine learning metamodeling simulation-based design The distinct treatment of dominated designs adopted by AFs, such as the EEI function and the proposed approach, generates...

... sampling strategy kriging metamodeling reliability in design References [1] Grieves , M. W. , 2005 , “ Product Lifecycle Management: The New Paradigm for Enterprises ,” Int. J. Prod. Dev. , 2 ( 1–2 ), pp. 71 – 84 . 10.1504/IJPD.2005.006669 [2] Miller , A. M...

... surrogate model nonlinearity integrated multi-fidelity surrogate correlation metamodeling At present, with the rapid development of computer technology, the methods of numerical analysis, such as the finite element analysis and computational fluid dynamics, have been routinely employed...

...Jie Liu; Yue Zhao; Fei Lei; Fei Ding Metamodel technology provides an efficient method to approximate complex engineering design problems. However, the approximation for high-dimensional problems usually requires a large number of samples for most traditional metamodeling methods, which leads...

... black-box dynamic optimization right-hand side function surrogate model dynamic hybrid sampling hypercube shrinking approximation-based optimal design metamodeling References [1] Zhang , Y. , Li , S. , and Liao , L. , 2019 , “ Near-Optimal Control of Nonlinear...

... dimension selection method, based on the sensitivity of the variables, has been developed in methods such as mode-pursuing sampling using discriminative coordinate perturbation [ 17 ] and partial metamodel optimization [ 18 ]. With the development of artificial intelligence (AI), more and more AI-based...

... 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...

... 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...

... 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...