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 26 articles , 1 standard article )

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  1. Ozcanan, Sedat; Atahan, Ali Osman: Minimization of accident severity index in concrete barrier designs using an ensemble of radial basis function metamodel-based optimization (2021)
  2. Kim, Sun Hye; Boukouvala, Fani: Machine learning-based surrogate modeling for data-driven optimization: a comparison of subset selection for regression techniques (2020)
  3. Wang, Chong: Evidence-theory-based uncertain parameter identification method for mechanical systems with imprecise information (2019)
  4. Wang, Chong; Matthies, Hermann G.: Epistemic uncertainty-based reliability analysis for engineering system with hybrid evidence and fuzzy variables (2019)
  5. Bradford, Eric; Schweidtmann, Artur M.; Lapkin, Alexei: Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm (2018)
  6. Eggensperger, Katharina; Lindauer, Marius; Hoos, Holger H.; Hutter, Frank; Leyton-Brown, Kevin: Efficient benchmarking of algorithm configurators via model-based surrogates (2018)
  7. Miriyala, Srinivas Soumitri; Subramanian, Venkat; Mitra, Kishalay: TRANSFORM-ANN for online optimization of complex industrial processes: casting process as case study (2018)
  8. Van Steenkiste, Tom; van der Herten, Joachim; Couckuyt, Ivo; Dhaene, Tom: Sequential sensitivity analysis of expensive black-box simulators with metamodelling (2018)
  9. Fouladinejad, Nariman; Fouladinejad, Nima; Abdul Jalil, Mohamad Kasim; Mohd Taib, Jamaludin: Decomposition-assisted computational technique based on surrogate modeling for real-time simulations (2017)
  10. Hamdi, Hamidreza; Couckuyt, Ivo; Sousa, Mario Costa; Dhaene, Tom: Gaussian processes for history-matching: application to an unconventional gas reservoir (2017)
  11. 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)
  12. Zaytsev, A.; Burnaev, E.: Large scale variable fidelity surrogate modeling (2017)
  13. Ito, Keiichi; Couckuyt, Ivo; Poles, Silvia; Dhaene, Tom: Variance-based interaction index measuring heteroscedasticity (2016)
  14. Peherstorfer, Benjamin; Willcox, Karen: Data-driven operator inference for nonintrusive projection-based model reduction (2016)
  15. Singh, Prashant; Couckuyt, Ivo; Elsayed, Khairy; Deschrijver, Dirk; Dhaene, Tom: Shape optimization of a cyclone separator using multi-objective surrogate-based optimization (2016)
  16. Ulaganathan, Selvakumar; Couckuyt, I.; Dhaene, T.; Degroote, J.; Laermans, E.: High dimensional kriging metamodelling utilising gradient information (2016)
  17. Wu, Jinglai; Luo, Zhen; Zheng, Jing; Jiang, Chao: Incremental modeling of a new high-order polynomial surrogate model (2016)
  18. Baran, M.; Bieniasz, L. K.: Experiments with an adaptive multicut-HDMR map generation for slowly varying continuous multivariate functions (2015)
  19. 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)
  20. Couckuyt, Ivo; Deschrijver, Dirk; Dhaene, Tom: Fast calculation of multiobjective probability of improvement and expected improvement criteria for Pareto optimization (2014)

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