ot-smt documentation

otSMT is a module of OpenTURNS implementing some methods to bind surrogate models from SMT into OpenTURNS PythonFunctions.

Available surrogate models from SMT:

• Least Squares Model

• Neural Network Model

• Radial Basis Function

• Inverse Distance Weighting

• Regularized minimal-energy tensor-product splines

• Second-order polynomial approximation

• Kriging

• Kriging Partial Least Squares (KPLS)

• KPLSK

• Gradient Enhanced KPLS

• Mixtures of Experts

Available multifidelity surrogate models from SMT:

• Multi-Fidelity Kriging

• Multi-Fidelity KPLS

• Multi-Fidelity KPLSK

Available mixed-variables surrogate models from SMT:

• Mixed Integer Kriging with Continuous Relaxation

• Mixed Integer Kriging with Gower Distance

Documentation about SMT can be found here

User documentation

• User manual

Examples

• Examples
  • Use of Least Squares Surrogate Model
  • Use of Gradient Enhanced Neural Network Model
  • Use of Radial Basis Function
  • Use of Inverse Distance Weighting
  • Use of Regularized minimal-energy tensor-product splines
  • Use of Second-order polynomial approximation
  • Use of Kriging
  • Use of KPLS
  • Use of KPLSK
  • Use of GEKPLS
  • Use of Mixture of Experts
  • Use of Multi-Fidelity Kriging
  • Use of Multi-Fidelity Kriging Partial Least Squares
  • Use of Multi-Fidelity Kriging Partial Least Squares K
  • Use of Mixed Variable Kriging

References

• Bouhlel, M. A., Hwang, J. T., Bartoli, N., Lafage, R., Morlier, J., & Martins, J. R. (2019). A Python surrogate modeling framework with derivatives. Advances in Engineering Software, 135, 102662.

Indices and tables

• Index

• Module Index

• Search Page