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Modeling, analysis, and optimization under uncertainties: a review
Abstract Design optimization of structural and multidisciplinary systems under uncertainty
has been an active area of research due to its evident advantages over deterministic design …
has been an active area of research due to its evident advantages over deterministic design …
Evidence-theory-based structural reliability analysis with epistemic uncertainty: a review
Z Zhang, C Jiang - Structural and Multidisciplinary Optimization, 2021 - Springer
Epistemic uncertainty widely exists in the early design stage of complex engineering
structures or throughout the full-life cycle of innovative structure design, which should be …
structures or throughout the full-life cycle of innovative structure design, which should be …
Mixed efficient global optimization for time-dependent reliability analysis
Time-dependent reliability analysis requires the use of the extreme value of a response. The
extreme value function is usually highly nonlinear, and traditional reliability methods, such …
extreme value function is usually highly nonlinear, and traditional reliability methods, such …
A general failure-pursuing sampling framework for surrogate-based reliability analysis
Abstract Design of experiment and active learning strategy are vital for the surrogate-based
reliability analysis. However, the existing sampling and modeling methods usually ignore …
reliability analysis. However, the existing sampling and modeling methods usually ignore …
Global sensitivity analysis-enhanced surrogate (GSAS) modeling for reliability analysis
An essential issue in surrogate model-based reliability analysis is the selection of training
points. Approaches such as efficient global reliability analysis (EGRA) and adaptive Kriging …
points. Approaches such as efficient global reliability analysis (EGRA) and adaptive Kriging …
A nested extreme response surface approach for time-dependent reliability-based design optimization
A primary concern in practical engineering design is ensuring high system reliability
throughout a product's lifecycle, which is subject to time-variant operating conditions and …
throughout a product's lifecycle, which is subject to time-variant operating conditions and …
Limit state Kriging modeling for reliability-based design optimization through classification uncertainty quantification
X Li, H Zhu, Z Chen, W Ming, Y Cao, W He… - Reliability engineering & …, 2022 - Elsevier
Reliability-based design optimization (RBDO) plays a vital role in considering the effect of
uncertainties in the optimal design variables on the production reliability. Kriging-assisted …
uncertainties in the optimal design variables on the production reliability. Kriging-assisted …
System reliability analysis with autocorrelated kriging predictions
When limit-state functions are highly nonlinear, traditional reliability methods, such as the
first-order and second-order reliability methods, are not accurate. Monte Carlo simulation …
first-order and second-order reliability methods, are not accurate. Monte Carlo simulation …
Deep learning for high-dimensional reliability analysis
High-dimensional reliability analysis remains a grand challenge since most of the existing
methods suffer from the curse of dimensionality. This paper introduces a novel high …
methods suffer from the curse of dimensionality. This paper introduces a novel high …
Reliability analysis with Monte Carlo simulation and dependent Kriging predictions
Reliability analysis is time consuming, and high efficiency could be maintained through the
integration of the Kriging method and Monte Carlo simulation (MCS). This Kriging-based …
integration of the Kriging method and Monte Carlo simulation (MCS). This Kriging-based …