Advertisement

Ensembles of Surrogate Models

  • Ping JiangEmail author
  • Qi Zhou
  • Xinyu Shao
Chapter
Part of the Springer Tracts in Mechanical Engineering book series (STME)

Abstract

An ensemble of surrogate models (EM) is a surrogate model composed of a series of surrogate models combined through a weighted sum. An EM can take advantage of each individual surrogate model to effectively increase the robustness of the prediction. The mathematical expression for an EM can be given as follows:

References

  1. Acar E, Rais-Rohani M (2009) Ensemble of metamodels with optimized weight factors. Struct Multidiscip Optim 37:279–294CrossRefGoogle Scholar
  2. Bishop CM (1995) Neural networks for pattern recognition. Oxford university pressGoogle Scholar
  3. Goel T, Haftka RT, Shyy W, Queipo NV (2007) Ensemble of surrogates. Struct Multidiscip Optim 33:199–216CrossRefGoogle Scholar
  4. Lee Y, Choi D-H (2014) Pointwise ensemble of meta-models using v nearest points cross-validation. Struct Multidiscip Optim 50:383–394CrossRefGoogle Scholar
  5. Liu H, Xu S, Wang X, Meng J, Yang S (2016) Optimal weighted pointwise ensemble of radial basis functions with different basis functions. AIAA J 3117–3133CrossRefGoogle Scholar
  6. Viana FA, Haftka RT, Steffen V (2009) Multiple surrogates: how cross-validation errors can help us to obtain the best predictor. Struct Multidiscip Optim 39:439–457CrossRefGoogle Scholar
  7. Zerpa LE, Queipo NV, Pintos S, Salager J-L (2005) An optimization methodology of alkaline–surfactant–polymer flooding processes using field scale numerical simulation and multiple surrogates. JoPS Eng 47:197–208Google Scholar
  8. Zhang J, Chowdhury S, Messac A (2012) An adaptive hybrid surrogate model. Struct Multidiscip Optim 46:223–238CrossRefGoogle Scholar
  9. Zhang J, Chowdhury S, Messac A, Castillo L (2013) Adaptive hybrid surrogate modeling for complex systems. AIAA J 51:643–656CrossRefGoogle Scholar
  10. Zhou XJ, Ma YZ, Li XF (2011) Ensemble of surrogates with recursive arithmetic average. 44:651–671Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.The State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and EngineeringHuazhong University of Science and TechnologyWuhanChina
  2. 2.School of Aerospace EngineeringHuazhong University of Science and TechnologyWuhanChina
  3. 3.The State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and EngineeringHuazhong University of Science and TechnologyWuhanChina

Personalised recommendations