The analysis of complex systems typically requires a large and diverse ensemble of models to confront the richness and complexity of the problem. Nevertheless, a common development and validation process of each individual model can be rigorous and time consuming. Because of limited time and resources, it is sometimes unrealistic to model every individual object in the population to capture the complex phenomena of the system. One such challenge arises in aerospace engineering when only a very limited number of aircraft performance models can be built to cover the aircraft fleet of the world for the environmental impact modeling of air transportation system. This paper considers the problem of selecting a small proportion of representative models to sufficiently cover and represent the population for more efficient and accurate systems analysis. At the intersection of unsupervised data mining and high-dimensional data analysis, the proposed methodology uses minimax and multi-criteria considerations to select a representative model portfolio at each budget level. Applying on the case study of representative aircraft models for environmental impact modeling, the benefits of the proposed method are demonstrated through both data visualization and quantitative metrics in multiple experiments. Potential limitations, extensions, and broader impacts of the method are also thoroughly discussed.


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    Title :

    Minimax and Multi-Criteria Selection of Representative Model Portfolios for Complex Systems Analysis


    Contributors:

    Published in:

    AIAA Journal ; 60 , 3 ; 1505-1521


    Publication date :

    2021-12-13


    Size :

    17 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English






    Selecting system portfolios

    Maier, M.W. / Singleton, G. / Fishenden, J. | IEEE | 2004


    Minimax studies

    Glasser, W. A. / Graham, K. D. / Harvey, C. A. | TIBKAT | 1966


    Minimax Reinforcement Learning

    Chakravorty, Suman / Hyland, David | AIAA | 2003