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Contents:

  • 1. Overview: Uranie in a nutshell
  • 2. The DataServer module
  • 3. The Sampler module
  • 4. The Launcher module
  • 5. The Modeler Module
    • 5.1. Introduction
    • 5.2. The TLinearRegression Class
    • 5.3. Chaos polynomial expansion
    • 5.4. The kriging method
  • 6. The Sensitivity module
  • 7. The Optimizer module
  • 8. The Relauncher module
  • 9. The Reoptimizer module
  • 10. The Metamodel Optimization module
  • 11. The Calibration module
  • 12. The Uncertainty modeler module
  • 13. Use-cases in Python
UserManual Python
  • 5. The Modeler Module
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5. The Modeler Module

This is a description of the Modeler module whose main goal is to produce a surrogate model from a database stored in a TDataServer.

  • 5.1. Introduction
  • 5.2. The TLinearRegression Class
  • 5.3. Chaos polynomial expansion
    • 5.3.1. Nisp in a nutshell
      • 5.3.1.1. The integration method
  • 5.4. The kriging method
    • 5.4.1. Construction of a kriging model
      • 5.4.1.1. Example: construction of a simple Kriging model
      • 5.4.1.2. Deterministic trend and bayesian prior
      • 5.4.1.3. Choice of the initial point of the optimisation
    • 5.4.2. Usage of a Kriging model
      • 5.4.2.1. Prediction of a new data set, one-by-one approach
      • 5.4.2.2. Prediction of a new data set, global approach
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