Surrogate Modeling

Over the years, the accuracy of engineering simulation software has improved significantly allowing simulation of systems at a finer level of detail. This evolution opens up usage of simulations for increasingly complex problems, but also increases the associated computational cost tremendously.

Surrogate modeling is an interdisciplinary research field that uses data-efficient machine learning to expedite the analysis and optimization of high-fidelity simulations what otherwise may take weeks or months. Surrogate models are fast-running approximations of complex time-consuming computer simulations. They are also known as response surface models (RSM), metamodels, proxy models or emulators.

Surrogate modeling bridges the gap between the numerical or experimental, and the analytical. Surrogate models are used for parametric studies, optimization, design-space exploration, visualization, prototyping, uncertainty quantification and sensitivity analysis.


Tom Dhaene, Dirk Deschrijver


Joachim van der Herten, Domenico Spina, Ivo Couckuyt, Tom Van Steenkiste, Nicolas Knudde,


  • ICON FORWARD, “Creating robust and reliable wireless networks for harsh industrial environments”
  • ICON SENCOM: “Smart energy consumption in manufacturing”
  • TETRA NEATH: “NEATH - Near-field meettechnieken voor het efficiënt oplossen van emc emissie problemen”,
  • ICON QOCON: “Quality Of service in COgnitive Networks”

Key publications