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Uncertainty Modeling for Engineering Applications pp 55–69Cite as

Data-Efficient Sensitivity Analysis with Surrogate Modeling

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Abstract

As performing many experiments and prototypes leads to a costly and long analysis process, scientists and engineers often rely on accurate simulators to reduce costs and improve efficiency. However, the computational demands of these simulators are also growing as their accuracy and complexity keeps increasing. Surrogate modeling is a powerful framework for data-efficient analysis of these simulators. A common use-case in engineering is sensitivity analysis to identify the importance of each of the inputs with regard to the output. In this work, we discuss surrogate modeling, sequential design, sensitivity analysis and how these three can be combined into a data-efficient sensitivity analysis method to accurately perform sensitivity analysis.

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Authors and Affiliations

  1. IDLab, Department of Information Technology, Ghent University - imec, Technologiepark-Zwijnaarde 15, 9052, Gent, Belgium

    Tom Van Steenkiste, Joachim van der Herten, Ivo Couckuyt & Tom Dhaene

Authors
  1. Tom Van Steenkiste
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  2. Joachim van der Herten
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  3. Ivo Couckuyt
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  4. Tom Dhaene
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Corresponding author

Correspondence to Tom Van Steenkiste .

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Editors and Affiliations

  1. Department of Electronics and Telecommunications (DET), Politecnico di Torino, Turin, Italy

    Flavio Canavero

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Van Steenkiste, T., van der Herten, J., Couckuyt, I., Dhaene, T. (2019). Data-Efficient Sensitivity Analysis with Surrogate Modeling. In: Canavero, F. (eds) Uncertainty Modeling for Engineering Applications. PoliTO Springer Series. Springer, Cham. https://doi.org/10.1007/978-3-030-04870-9_4

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