Surrogate-assisted global sensitivity analysis: an overview

K Cheng, Z Lu, C Ling, S Zhou - Structural and Multidisciplinary …, 2020 - Springer
Surrogate models are popular tool to approximate the functional relationship of expensive
simulation models in multiple scientific and engineering disciplines. Successful use of …

An efficient multi-fidelity Kriging surrogate model-based method for global sensitivity analysis

X Shang, L Su, H Fang, B Zeng, Z Zhang - Reliability Engineering & System …, 2023 - Elsevier
Global sensitivity analysis (GSA), particularly for Sobol index, is a powerful tool to quantify
the variation of model response sourced from the uncertainty of input variables over the …

Structural reliability analysis based on ensemble learning of surrogate models

K Cheng, Z Lu - Structural Safety, 2020 - Elsevier
Assessing the failure probability of complex structure is a difficult task in presence of various
uncertainties. In this paper, a new adaptive approach is developed for reliability analysis by …

A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems

K Kontolati, D Loukrezis, DG Giovanis… - Journal of …, 2022 - Elsevier
Constructing surrogate models for uncertainty quantification (UQ) on complex partial
differential equations (PDEs) having inherently high-dimensional O (10 n), n≥ 2, stochastic …

Active sparse polynomial chaos expansion for system reliability analysis

Y Zhou, Z Lu, W Yun - Reliability Engineering & System Safety, 2020 - Elsevier
Surrogate model techniques have been widely used for structural systems with expensively
evaluated simulations. However, their application to system reliability problems meets the …

Robust optimization of a marine current turbine using a novel robustness criterion

MS Karimi, R Mohammadi, M Raisee… - Energy Conversion and …, 2023 - Elsevier
The present paper aims to establish a systematic robust optimization framework for the
hydrodynamic performance of marine current turbines against uncertain conditions. To this …

基于自适应多可信度多项式混沌-Kriging 模型的高效气动优化方法

赵欢 - 力学学报, 2023 - lxxb.cstam.org.cn
多可信度代理模型已经成为提高基于代理模型的优化算法效率和可信度水平最有效的手段之一.
然而目前流行的co-Kriging 和分层Kriging (HK) 等多可信度代理模型泛化能力不足 …

Efficient aerodynamic analysis and optimization under uncertainty using multi-fidelity polynomial chaos-Kriging surrogate model

H Zhao, ZH Gao, L Xia - Computers & Fluids, 2022 - Elsevier
Surrogate model has been extensively employed in uncertainty-based design optimization
(UBDO) for computationally expensive engineering problems. However, it often causes …

Adaptive multi-fidelity sparse polynomial chaos-Kriging metamodeling for global approximation of aerodynamic data

H Zhao, Z Gao, F Xu, L Xia - Structural and Multidisciplinary Optimization, 2021 - Springer
The multi-fidelity metamodeling method can dramatically improve the efficiency of
metamodeling for computationally expensive engineering problems when multiple levels of …

[HTML][HTML] Effects of uncertainties in positioning of PIV plane on validation of CFD results of a high-head Francis turbine model

S Salehi, H Nilsson - Renewable Energy, 2022 - Elsevier
Abstract The use of Computational Fluid Dynamics (CFD) for the design of modern hydraulic
turbines has increased and matured significantly the last decades. More recently, CFD is …