Surrogate modeling and optimization for scientific machine learning (SciML)
131 Stars
Updated Last
1 Year Ago
Started In
May 2019



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
  • Kronecker
  • Golden
  • Random

ALL the currently available surrogate models:

  • Kriging
  • Kriging using Stheno
  • Radial Basis
  • Wendland
  • Linear
  • Second Order Polynomial
  • Support Vector Machines (Wait for LIBSVM resolution)
  • Neural Networks
  • Random Forests
  • Lobachevsky
  • Inverse-distance
  • Polynomial expansions
  • Variable fidelity
  • Mixture of experts (Waiting GaussianMixtures package to work on v1.5)
  • Earth
  • Gradient Enhanced Kriging

ALL the currently available optimization methods:

  • SRBF
  • LCBS
  • EI
  • SOP
  • Multi-optimization: SMB and RTEA

Installing Surrogates package

using Pkg