... performance of the proposed method with a dimensionality reduction-based surrogate modeling framework on a representative linear elastic ellipsoidal void problem with uniaxial loading. The data structure focuses on the low-data regime (fewer than 100 data points) and includes the parameterized geometry...

... 20 06 2024 19 07 2024 Graphical Abstract Figure multidisciplinary design optimization robust design surrogate modeling artificial neural networks turbocompressor design micro-turbomachinery gas-bearings herringbone grooved journal bearings manufacturing deviations...

... solutions. Of the examples provided, this is the worst performance of the proposed approach compared to AA-MORO, except in terms of OS. As stated before, AA-MORO has a larger emphasis on the exploitation of surrogate models than exploration. For a simple case like this one, that exploitation is rewarded...

...Jeong Woo Park; Ikjin Lee In engineering applications of sampling-based reliability-based design optimization (RBDO), the Monte Carlo simulation (MCS) using a surrogate model of the performance function is mainly used for the probability of failure calculation and sensitivity analysis. However...

... for publication in the J ournal of M echanical D esign . 08 10 2020 09 07 2021 09 07 2021 11 08 2021 Bayesian optimization surrogate modeling multi-objective optimization weighted Tchebycheff Bayesian regression metamodeling stress in design Nuclear Energy...

...Yongsu Jung; Kyeonghwan Kang; Hyunkyoo Cho; Ikjin Lee Even though many efforts have been devoted to effective strategies to build accurate surrogate models, surrogate model uncertainty is inevitable due to a limited number of available simulation samples. Therefore, the surrogate model uncertainty...

... ) ) considers the interpolation uncertainty that will be reduced to zero once a sample is observed at the new spatial location x exp ( j ) , while replicating at x ¯ i * ( j ) sample can only reduce the intrinsic modeling uncertainty that enters the surrogate model...

...Andrew L. Kaminsky; Yi Wang; Kapil Pant Surrogate models can be used to approximate complex systems at a reduced cost and are widely used when data generation is expensive or time consuming. The accuracy of these models is dependent on the samples used to create them. Therefore, proper sample...

...Jie Zhang; Souma Chowdhury; Ali Mehmani; Achille Messac Both global and local error measure metrics were used to assess the accuracy of surrogate models. In this paper, the leave-one-out cross-validation errors at all training points were used as local error measures. When compared with errors...

... model of simulations of multistage assembly processes. At the heart of this approach is a multiple-input-multiple-output surrogate modeling framework that uses a recently developed integrated emulation technique. The unique feature of this technique is that the surrogate models for multiple outputs...

... engineering Power Switch Modules Aeronautic Applications Finite Elements Modeling Surrogate Modeling Lifetime Analysis Structural Reliability One of the current trends in the aircraft industry is the increasing use of electrical systems in replacement of mechanical, hydraulic, and pneumatic...

..., and surrogate models to arrive at robust designs on a limited computational budget. The multiobjective formulation used here allows explicit trade-off between the mean and variance of the performance to be carried out. Detailed numerical studies are presented for robust geometric design of a typical compressor...