The Surrogate Modeling Toolbox (SUMO Toolbox) is a Matlab toolbox that automatically builds accurate surrogate models (also known as metamodels or response surface models) of a given data source (simulation code, data set, script, ...) within the accuracy and time constraints set by the user. In doing so the toolbox minimizes the number of data points (which it chooses automatically) since they are usually expensive. The toolbox tries to be as adaptive and autonomous as possible, requiring no user input besides some initial configuration.

References in zbMATH (referenced in 17 articles , 1 standard article )

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  1. Bradford, Eric; Schweidtmann, Artur M.; Lapkin, Alexei: Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm (2018)
  2. Eggensperger, Katharina; Lindauer, Marius; Hoos, Holger H.; Hutter, Frank; Leyton-Brown, Kevin: Efficient benchmarking of algorithm configurators via model-based surrogates (2018)
  3. Miriyala, Srinivas Soumitri; Subramanian, Venkat; Mitra, Kishalay: TRANSFORM-ANN for online optimization of complex industrial processes: casting process as case study (2018)
  4. Fouladinejad, Nariman; Fouladinejad, Nima; Abdul Jalil, Mohamad Kasim; Mohd Taib, Jamaludin: Decomposition-assisted computational technique based on surrogate modeling for real-time simulations (2017)
  5. Hamdi, Hamidreza; Couckuyt, Ivo; Sousa, Mario Costa; Dhaene, Tom: Gaussian processes for history-matching: application to an unconventional gas reservoir (2017)
  6. Singh, Prashant; Couckuyt, Ivo; Elsayed, Khairy; Deschrijver, Dirk; Dhaene, Tom: Multi-objective geometry optimization of a gas cyclone using triple-fidelity co-Kriging surrogate models (2017)
  7. Zaytsev, A.; Burnaev, E.: Large scale variable fidelity surrogate modeling (2017)
  8. Ito, Keiichi; Couckuyt, Ivo; Poles, Silvia; Dhaene, Tom: Variance-based interaction index measuring heteroscedasticity (2016)
  9. Baran, M.; Bieniasz, L. K.: Experiments with an adaptive multicut-HDMR map generation for slowly varying continuous multivariate functions (2015)
  10. van der Herten, J.; Couckuyt, I.; Deschrijver, D.; Dhaene, T.: A fuzzy hybrid sequential design strategy for global surrogate modeling of high-dimensional computer experiments (2015)
  11. Couckuyt, Ivo; Deschrijver, Dirk; Dhaene, Tom: Fast calculation of multiobjective probability of improvement and expected improvement criteria for Pareto optimization (2014)
  12. Degroote, Joris; Hojjat, Majid; Stavropoulou, Electra; Wüchner, Roland; Bletzinger, Kai-Uwe: Partitioned solution of an unsteady adjoint for strongly coupled fluid-structure interactions and application to parameter identification of a one-dimensional problem (2013)
  13. Degroote, Joris; Couckuyt, Ivo; Vierendeels, Jan; Segers, Patrick; Dhaene, Tom: Inverse modelling of an aneurysm’s stiffness using surrogate-based optimization and fluid-structure interaction simulations (2012)
  14. De Tommasi, Luciano; Beelen, Theo G. J.; Sevat, Marcel F.; Rommes, Joost; ter Maten, E. Jan W.: Multi-objective optimization of RF circuit blocks via surrogate models and NBI and SPEA2 methods (2012)
  15. Crombecq, K.; Laermans, E.; Dhaene, T.: Efficient space-filling and non-collapsing sequential design strategies for simulation-based modeling (2011) ioport
  16. De Tommasi, Luciano; Gorissen, Dirk; Croon, Jeroen A.; Dhaene, Tom: Surrogate modeling of RF circuit blocks (2010)
  17. Gorissen, Dirk; Couckuyt, Ivo; Demeester, Piet; Dhaene, Tom; Crombecq, Karel: A surrogate modeling and adaptive sampling toolbox for computer based design (2010) ioport