... process F ( t ), it is necessary to use six standard normal distribution random variables Z = [ Z 1 , Z 2 , … , Z 6 ] based on Eq. (11) (i.e., p = 6). Additionally, the number of initial samples for surrogate modeling is set to 40 in this case. Moreover, the effect...

...Kunpeng Li; Xiwang He; Liye Lv; Jiaxiang Zhu; Guangbo Hao; Haiyang Li; Xueguan Song Surrogate model provides a promising way to reasonably approximate complex underlying relationships between system parameters. However, the expensive modeling cost, especially in large problem sizes, hinders its...

...Yin Liu; Shuo Wang; Kunpeng Li; Wei Sun; Xueguan Song Most practical multi-objective optimization problems are often characterized by two or more expensive and conflicting objectives, which require time-consuming simulations. Commonly used algorithms construct a surrogate model of each objective...

... surrogate modeling methods have been developed to overcome the computational challenge in simulation-based mobility prediction, it is very challenging for a single surrogate model to accurately capture the complicated vehicle dynamics. With a focus on vertical acceleration of an AGV under off-road...

... is compared with those of various surrogate models (interpolation models, regression models, and multi-fidelity models) on both numerical and practical engineering problems. In the experiments, the proposed MTDF model demonstrates higher performance than the other benchmark models. The effects of noise level...

...: T20100767@csuft.edu.cn approximation-based optimal design data-driven design machine learning surrogate model National Natural Science Foundation of China 10.13039/501100001809 51621004 Uncertainties [ 1 ] of engineering problems usually arise from the constitutive model...

... the continuous field of the simulation by limited sensor data to obtain accurate results for the digital twin. Then, single-fidelity surrogate models are built on the whole domain using the calibrated results of the MFS-MLS as training samples and sensor data as inputs to predict and visualize the quantities...

...Eamon Whalen; Caitlin Mueller Surrogate models have several uses in engineering design, including speeding up design optimization, noise reduction, test measurement interpolation, gradient estimation, portability, and protection of intellectual property. Traditionally, surrogate models require...

... planning. A concept of MMR is first proposed to quantify reliability of a mission path which passes through different types of soils. A single-loop Kriging surrogate modeling method is then employed to overcome the computational challenge in MMR assessment caused by expensive mobility simulations. Built...

...Yiming Zhang; Nam H. Kim; Raphael T. Haftka A surrogate model is a common tool to approximate system response at untested points for design space exploration. Adaptive sampling has been studied for improving the accuracy of surrogates iteratively by introducing additional samples (simulations...

... Automation Committee of ASME for publication in the J ournal of M echanical D esign . 25 12 2018 05 06 2019 08 06 2019 26 06 2019 hybrid infilling strategy Go-inspired adaptive sampling strategy surrogate model Surrogate modeling techniques have been widely applied...

... to imperfections in input distribution models, simulation models, and surrogate models, as well as a limited number of output testing due to cost. Thus, the estimated output distributions and their corresponding reliabilities would become uncertain. One way to treat epistemic uncertainty is to use a hierarchical...

... of the problem and the difficulty of simplifying the dimension chain equation, this paper investigates the use of the Rackwitz–Fiessler (R–F) reliability analysis method and several surrogate model methods, respectively. The tolerance analysis results obtained for a landing gear assembly problem using the R–F...

...Ikjin Lee; K. K. Choi; Yoojeong Noh; Liang Zhao; David Gorsich This study presents a methodology for computing stochastic sensitivities with respect to the design variables, which are the mean values of the input correlated random variables. Assuming that an accurate surrogate model is available...

...Ying Xiong; Wei Chen; Kwok-Leung Tsui Computational models with variable fidelity have been widely used in engineering design. To alleviate the computational burden, surrogate models are used for optimization without directly invoking expensive high-fidelity simulations. In this work, a model...

...Tiefu Shao; Sundar Krishnamurty This paper addresses the critical issue of effectiveness and efficiency in simulation-based optimization using surrogate models as predictive models in engineering design. Specifically, it presents a novel clustering-based multilocation search (CMLS) procedure...