Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
A scalable problem to benchmark robust multidisciplinary design optimization techniques
A scalable problem to benchmark robust multidisciplinary design optimization (RMDO) algorithms is proposed. This allows the user to choose the number of disciplines, the dimensions of the coupling and design variables and the extent of the feasible domain. After a description of the mathematical background, a deterministic version of the scalable problem is defined and the conditions on the existence and uniqueness of the solution are given. Then, this deterministic scalable problem is made uncertain by adding random variables to the coupling equations. Under classical assumptions, the existence and uniqueness of the solution of this RMDO problem is guaranteed. This solution can be easily computed with a quadratic programming algorithm and serves as a reference to assess the performance of RMDO algorithms. This scalable problem has been implemented in the open-source library GEMSEO and tested with two techniques of statistics estimation: Monte-Carlo sampling and Taylor polynomials.
A scalable problem to benchmark robust multidisciplinary design optimization techniques
A scalable problem to benchmark robust multidisciplinary design optimization (RMDO) algorithms is proposed. This allows the user to choose the number of disciplines, the dimensions of the coupling and design variables and the extent of the feasible domain. After a description of the mathematical background, a deterministic version of the scalable problem is defined and the conditions on the existence and uniqueness of the solution are given. Then, this deterministic scalable problem is made uncertain by adding random variables to the coupling equations. Under classical assumptions, the existence and uniqueness of the solution of this RMDO problem is guaranteed. This solution can be easily computed with a quadratic programming algorithm and serves as a reference to assess the performance of RMDO algorithms. This scalable problem has been implemented in the open-source library GEMSEO and tested with two techniques of statistics estimation: Monte-Carlo sampling and Taylor polynomials.
A scalable problem to benchmark robust multidisciplinary design optimization techniques
Optim Eng
Aziz-Alaoui, Amine (Autor:in) / Roustant, Olivier (Autor:in) / De Lozzo, Matthias (Autor:in)
Optimization and Engineering ; 25 ; 941-958
01.06.2024
18 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Multidisciplinary design optimization , Robust optimization , Scalable problem , Benchmarking , Uncertainty quantification , Quadratic programming Mathematics , Optimization , Engineering, general , Systems Theory, Control , Environmental Management , Operations Research/Decision Theory , Financial Engineering , Mathematics and Statistics
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