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Link to original content: https://doi.org/10.1007/s00366-009-0138-1
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Multiobjective global surrogate modeling, dealing with the 5-percent problem

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Abstract

When dealing with computationally expensive simulation codes or process measurement data, surrogate modeling methods are firmly established as facilitators for design space exploration, sensitivity analysis, visualization, prototyping and optimization. Typically the model parameter (=hyperparameter) optimization problem as part of global surrogate modeling is formulated in a single objective way. Models are generated according to a single objective (accuracy). However, this requires an engineer to determine a single accuracy target and measure upfront, which is hard to do if the behavior of the response is unknown. Likewise, the different outputs of a multi-output system are typically modeled separately by independent models. Again, a multiobjective approach would benefit the domain expert by giving information about output correlation and enabling automatic model type selection for each output dynamically. With this paper the authors attempt to increase awareness of the subtleties involved and discuss a number of solutions and applications. In particular, we present a multiobjective framework for global surrogate model generation to help tackle both problems and that is applicable in both the static and sequential design (adaptive sampling) case.

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Notes

  1. In this case, the samples are noisy but the same phenomenon can occur with noise free data and a validation set.

  2. In some cases, discretization and convergence noise may be present, the magnitude depending on the application.

  3. A movie showing the evolution is available at http://www.sumolab.blogspot.com/.

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Acknowledgments

The authors wish to thank Markus Ganser and Karen Grossenbacher from BMW motor company for making the automotive data available and the fruitful discussions. The authors also thank Jeroen Croon from the NXP-TSMC Research Center, Device Modeling Department, Eindhoven, The Netherlands for the LNA simulation code. Finally, the authors also thank Matthias Ihme from Standford University for providing the chemistry combustion data.

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  1. Department of Information Technology (INTEC), IBBT, Ghent University, Gaston Crommenlaan 8, 9050, Ghent, Belgium

    Dirk Gorissen, Ivo Couckuyt, Eric Laermans & Tom Dhaene

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Correspondence to Dirk Gorissen.

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Gorissen, D., Couckuyt, I., Laermans, E. et al. Multiobjective global surrogate modeling, dealing with the 5-percent problem. Engineering with Computers 26, 81–98 (2010). https://doi.org/10.1007/s00366-009-0138-1

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