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 …
Efficient decoupling-assisted evolutionary/metaheuristic framework for expensive reliability-based design optimization problems
Reliability-based design optimization (RBDO) algorithm is to minimize the objective under
the probabilistic factors. While gradient-based and classical evolutionary RBDO algorithms …
the probabilistic factors. While gradient-based and classical evolutionary RBDO algorithms …
A general fidelity transformation framework for reliability-based design optimization with arbitrary precision
Reliability-based design optimization (RBDO) offers a powerful tool to handle optimization
problems with inherently unavoidable uncertainty factors. However, solving the engineering …
problems with inherently unavoidable uncertainty factors. However, solving the engineering …
[HTML][HTML] Speed optimisation and reliability analysis of a self-propelled capsule robot moving in an uncertain frictional environment
The dynamics of a self-propelled capsule robot for small-bowel endoscopy driven by its
internal vibro-impact excitation is studied in this paper. Due to its complex anatomy, the …
internal vibro-impact excitation is studied in this paper. Due to its complex anatomy, the …
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 …
高置信水平结构逆可靠度分析与优化方法研究进展
郝鹏， 杨浩， 冯少军， 王博 - 力学学报, 2023 - lxxb.cstam.org.cn
不确定性客观存在于工程结构生产制造以及服役等各环节之中并对结构的承载性能影响显著.
特别是对于服役条件苛刻的航空航天承载装备, 在设计阶段考虑多源不确定性的影响对于结构 …
特别是对于服役条件苛刻的航空航天承载装备, 在设计阶段考虑多源不确定性的影响对于结构 …
Optimal design of experiments for optimization-based model calibration using Fisher information matrix
Statistical model calibration to infer unknown model parameters and model bias has been
widely developed through comparison between simulation response and experimental data …
widely developed through comparison between simulation response and experimental data …
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 …