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 …
An efficient two-stage water cycle algorithm for complex reliability-based design optimization problems
The reliability-based design optimization (RBDO) problem considers the necessary
uncertainty of measurements within the scope of planning to minimize the design objective …
uncertainty of measurements within the scope of planning to minimize the design objective …
Statistical model calibration and design optimization under aleatory and epistemic uncertainty
Statistical model calibration is a framework for inference on unknown model parameters and
modeling discrepancy between simulation and experiment through an inverse method in the …
modeling discrepancy between simulation and experiment through an inverse method in the …
Confidence-based design optimization for a more conservative optimum under surrogate model uncertainty caused by Gaussian process
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 …
surrogate models, surrogate model uncertainty is inevitable due to a limited number of …
A confidence-based reliability optimization with single loop strategy and second-order reliability method
The statistical model is commonly used in the reliability-based design optimization (RBDO).
However, it is difficult to obtain sufficient data to construct a reasonable statistical model in …
However, it is difficult to obtain sufficient data to construct a reasonable statistical model in …
A sequential single-loop reliability optimization and confidence analysis method
P Hao, H Yang, H Yang, Y Zhang, Y Wang… - Computer Methods in …, 2022 - Elsevier
In practical engineering problems, it is frequently challenging to collect sufficient data to
construct high-precision probabilistic models. In this case, probabilistic models typically …
construct high-precision probabilistic models. In this case, probabilistic models typically …
Hyperstatic and redundancy thresholds in truss topology optimization considering progressive collapse due to aleatory and epistemic uncertainties
Optimization leads to specialized structures which are not robust to disturbance events like
unanticipated abnormal loading or human errors. Typical reliability-based and robust …
unanticipated abnormal loading or human errors. Typical reliability-based and robust …
Construction of adaptive Kriging metamodel for failure probability estimation considering the uncertainties of distribution parameters
A critical problem in engineering reliability analysis is obtaining an accurate failure
probability with a high computational efficiency. This study aims to present failure probability …
probability with a high computational efficiency. This study aims to present failure probability …
Confidence-based reliability assessment considering limited numbers of both input and output test data
Simulation-based methods can be used for accurate uncertainty quantification and
prediction of the reliability of a physical system under the following assumptions:(1) accurate …
prediction of the reliability of a physical system under the following assumptions:(1) accurate …
Confidence-based design optimization using multivariate kernel density estimation under insufficient input data
The uncertainty quantification of the input statistical model in reliability-based design
optimization (RBDO) has been widely investigated for accurate reliability analysis, and it …
optimization (RBDO) has been widely investigated for accurate reliability analysis, and it …