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Surrogate Models for Mixed Discrete continuous Variables

Written By Wednesday, 19 October 2022 Add Comment Edit

Abstract

Surrogate modeling is an increasingly popular tool for engineering design as it enables to model the performance of very complex systems with a limited computational cost. A large number of techniques exists for the surrogate modeling of continuous functions, however, only a very few methods for the surrogate modeling of mixed continuous/discrete functions have been developed. In this chapter, existing adaptations and variants of Gaussian process-based surrogate modeling techniques for mixed continuous/discrete variables are described, discussed and compared on several analytical test-cases and aerospace design problems.

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Author information

Authors and Affiliations

  1. ONERA/DTIS, Université Paris Saclaym, CNES, Direction des lanceurs, University of Lille, Lille, France

    Julien Pelamatti

  2. ONERA/DTIS, Université Paris Saclay, 91123, Palaiseau Cedex, France

    Loïc Brevault & Mathieu Balesdent

  3. Polytech Lille – University of Lille CNRS/CRIStAL, Inria Lille – Nord Europe, 59650, Villeneuve d'Ascq, Lille, France

    El-Ghazali Talbi

  4. CNES, Diréction des lanceurs, 75612, Paris Cedex, Paris, France

    Yannick Guerin

Corresponding author

Correspondence to El-Ghazali Talbi .

Editor information

Editors and Affiliations

  1. TH Köln, Cologne, Germany

    Prof. Thomas Bartz-Beielstein

  2. Jožef Stefan Institute, Ljubljana, Slovenia

    Prof. Bogdan Filipič

  3. Jožef Stefan Institute, Ljubljana, Slovenia

    Prof. Peter Korošec

  4. University Lille, Lille, France

    Prof. El-Ghazali Talbi

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Pelamatti, J., Brevault, L., Balesdent, M., Talbi, EG., Guerin, Y. (2020). Overview and Comparison of Gaussian Process-Based Surrogate Models for Mixed Continuous and Discrete Variables: Application on Aerospace Design Problems. In: Bartz-Beielstein, T., Filipič, B., Korošec, P., Talbi, EG. (eds) High-Performance Simulation-Based Optimization. Studies in Computational Intelligence, vol 833. Springer, Cham. https://doi.org/10.1007/978-3-030-18764-4_9

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