58 Stars
Updated Last
10 Months Ago
Started In
May 2019



Build Status Build status Coverage Status Stable dev

A surrogate model is an approximation method that mimics the behavior of a computationally expensive simulation. In more mathematical terms: suppose we are attempting to optimize a function f(p), but each calculation of f is very expensive. It may be the case we need to solve a PDE for each point or use advanced numerical linear algebra machinery, which is usually costly. The idea is then to develop a surrogate model g which approximates f by training on previous data collected from evaluations of f. The construction of a surrogate model can be seen as a three-step process:

  1. Sample selection
  2. Construction of the surrogate model
  3. Surrogate optimization

ALL the currently available sampling methods:

  • Grid
  • Uniform
  • Sobol
  • Latin Hypercube
  • Low Discrepancy
  • Random

ALL the currently available surrogate models:

  • Kriging
  • Radial Basis Function
  • Linear
  • Second Order Polynomial
  • Support Vector Machines (SVM)
  • Artificial Neural Networks
  • Random Forests
  • Lobachesky
  • Inverse-distance

ALL the currently available optimization methods:

  • SRBF
  • LCBS
  • EI

Installing Surrogates package

using Pkg

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