Add to ORKGAbstract. Robust design optimization is a modeling methodology, com-bined with a suite of computational tools, which is aimed to solve prob-lems where some kind of uncertainty occurs in the data or in the model. This paper explores robust optimization complexity in the multiobjective case, describing a new approach by means of Polynomial Chaos expan-sions (PCE). The aim of this paper is to demonstrate that the use of PCE may help and speed up the optimization process if compared to standard approaches such as Monte Carlo and Latin Hypercube sampling
This chapter describes the application of a computationally efficient uncertainty quantification app...
Uncertainty exists widely in engineering design. As one of the key components of engineering design,...
The problem of robust design optimization consists in the search for technical solutions that can be...
Robust design optimization is a modeling methodology, combined with a suite of computational tools, ...
The study of robust design methodologies and techniques has become a new topical area in design opti...
This thesis aims at Multi-Objective Optimization under Uncertainty in structural design. We investig...
This thesis aims at Multi-Objective Optimization under Uncertainty in structural design. We investig...
Evolutionary algorithms are powerful optimizers often used to explore the trade-off between performa...
Optimization is the process to find the optimal solution from possible solutions. However, in many p...
To improve the efficiency of solving uncertainty design optimization problems, a gradient-based opti...
International audienceThis work illustrates a practical and efficient method for performing multi-ob...
Uncertainties associated with either the operating conditions and/or man- ufacturing imperfections f...
The objective of this paper is to introduce a computationally efficient and accurate approach for ro...
For many engineering problems, reliability and robustness are far more important than the nominal pe...
For many engineering problems, reliability and robustness are far more important than the nominal pe...
This chapter describes the application of a computationally efficient uncertainty quantification app...
Uncertainty exists widely in engineering design. As one of the key components of engineering design,...
The problem of robust design optimization consists in the search for technical solutions that can be...
Robust design optimization is a modeling methodology, combined with a suite of computational tools, ...
The study of robust design methodologies and techniques has become a new topical area in design opti...
This thesis aims at Multi-Objective Optimization under Uncertainty in structural design. We investig...
This thesis aims at Multi-Objective Optimization under Uncertainty in structural design. We investig...
Evolutionary algorithms are powerful optimizers often used to explore the trade-off between performa...
Optimization is the process to find the optimal solution from possible solutions. However, in many p...
To improve the efficiency of solving uncertainty design optimization problems, a gradient-based opti...
International audienceThis work illustrates a practical and efficient method for performing multi-ob...
Uncertainties associated with either the operating conditions and/or man- ufacturing imperfections f...
The objective of this paper is to introduce a computationally efficient and accurate approach for ro...
For many engineering problems, reliability and robustness are far more important than the nominal pe...
For many engineering problems, reliability and robustness are far more important than the nominal pe...
This chapter describes the application of a computationally efficient uncertainty quantification app...
Uncertainty exists widely in engineering design. As one of the key components of engineering design,...
The problem of robust design optimization consists in the search for technical solutions that can be...