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- 01GPKN0YRNFPP3J3AW0DCZCW47 classification A1.
- 01GPKN0YRNFPP3J3AW0DCZCW47 date "2022".
- 01GPKN0YRNFPP3J3AW0DCZCW47 language "eng".
- 01GPKN0YRNFPP3J3AW0DCZCW47 type journalArticle.
- 01GPKN0YRNFPP3J3AW0DCZCW47 hasPart 01GPKN2ZV6CQ3RQAKSRPHD6396.pdf.
- 01GPKN0YRNFPP3J3AW0DCZCW47 subject "Technology and Engineering".
- 01GPKN0YRNFPP3J3AW0DCZCW47 doi "10.2514/1.J061112".
- 01GPKN0YRNFPP3J3AW0DCZCW47 issn "0001-1452".
- 01GPKN0YRNFPP3J3AW0DCZCW47 issn "1533-385X".
- 01GPKN0YRNFPP3J3AW0DCZCW47 issue "11".
- 01GPKN0YRNFPP3J3AW0DCZCW47 volume "60".
- 01GPKN0YRNFPP3J3AW0DCZCW47 abstract "In this work, optimization-under-uncertainty (OUU) is treated by simultaneously minimizing the mean of the objective and its variance due to variability of design variables and/or parameters in a multi-objective setting, while simultaneously ensuring that the minimal probability of constraint failure is met. This allows the designer to choose its robustness level without the need to repeat the optimization as typically encountered when formulated as a single objective and ensuring that the system will not fail with a prescribed probability. To account for the computational cost that is often encountered in OUU problems, the problem is fitted in a Bayesian optimization framework. The use of surrogate modeling techniques to efficiently solve problems under uncertainty has effectively found its way in the optimization community leading to surrogate-assisted OUU schemes. The surrogates are often considered cheap-to-sample black-boxes and are sampled to obtain the desired quantities of interest. However, since the analytical formulation of the surrogates is known, the mean square predictive error of the quantities of interest can be derived. To obtain these quantities without sampling, an analytical uncertainty propagation and reliability analysis through the surrogate is presented. The multi-objective Bayesian optimization framework and the analytical uncertainty propagation and reliability analysis are linked together through the formulation of the reliability-based robust expected improvement. To further enhance the efficiency of the approach, the Bayesian optimization method is solved in an asynchronous manner. In doing so the novel Surrogate-assisted Asynchronous Multi-objective optimization under Uncertainty framework for Robust and reliable solutions to Applications in Industry (SAMURAI) scheme is defined. The method is applied to a number of case studies and the design of a low-Re airfoil for blended-wing-bodies, which proves the effectiveness of the novel methodology.".
- 01GPKN0YRNFPP3J3AW0DCZCW47 author 2AF9E22C-F0EE-11E1-A9DE-61C894A0A6B4.
- 01GPKN0YRNFPP3J3AW0DCZCW47 author F7378570-F0ED-11E1-A9DE-61C894A0A6B4.
- 01GPKN0YRNFPP3J3AW0DCZCW47 author F8569310-F0ED-11E1-A9DE-61C894A0A6B4.
- 01GPKN0YRNFPP3J3AW0DCZCW47 author FC259E64-F0ED-11E1-A9DE-61C894A0A6B4.
- 01GPKN0YRNFPP3J3AW0DCZCW47 dateCreated "2023-01-12T18:49:07Z".
- 01GPKN0YRNFPP3J3AW0DCZCW47 dateModified "2024-07-09T07:44:06Z".
- 01GPKN0YRNFPP3J3AW0DCZCW47 name "SAMURAI : a new asynchronous Bayesian optimization technique for optimization-under-uncertainty".
- 01GPKN0YRNFPP3J3AW0DCZCW47 pagination urn:uuid:fc918ca5-ffb8-44c0-9d82-59b25e847158.
- 01GPKN0YRNFPP3J3AW0DCZCW47 sameAs LU-01GPKN0YRNFPP3J3AW0DCZCW47.
- 01GPKN0YRNFPP3J3AW0DCZCW47 sourceOrganization urn:uuid:111f5661-91f5-4dac-b506-5f9ac0b8bfe8.
- 01GPKN0YRNFPP3J3AW0DCZCW47 sourceOrganization urn:uuid:cd9ed6e5-ba48-4be7-baf4-1bc2942efbae.
- 01GPKN0YRNFPP3J3AW0DCZCW47 type A1.