Many engineering design problems need to take into account manufacturing tolerances. The goal is then to identify a solution that is robust in the sense of providing good expected performance despite possible disturbances to its design variables. In this chapter, we propose a Bayesian optimization algorithm to tackle this problem. We focus on the simple Direct Robustness Approximation idea from Le and Branke (Bayesian optimization searching for robust solutions. In: 2020 Winter Simulation Conference (WSC) (2020)) and make the following three contributions. First, we demonstrate that using Latin Hypercube Sampling to estimate solution quality improves the quality of solutions found. Second, we consider the case where there is disturbance already during the design stage. And finally, we examine the use of stochastic Kriging rather than the standard Gaussian process as the surrogate model.


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    Titel :

    Bayesian Optimization for Robust Solutions Under Uncertain Input


    Weitere Titelangaben:

    Space Technol.Proceedings


    Beteiligte:
    Vasile, Massimiliano (Herausgeber:in) / Quagliarella, Domenico (Herausgeber:in) / Le, Hoai Phuong (Autor:in) / Branke, Juergen (Autor:in)

    Kongress:

    International Conference on Uncertainty Quantification & Optimisation ; 2020 ; Brussels, Belgium November 17, 2020 - November 20, 2020



    Erscheinungsdatum :

    16.07.2021


    Format / Umfang :

    15 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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