Surrogate modeling and optimization for scientific machine learning (SciML)
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May 2019


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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

Sampling can be done through QuasiMonteCarlo.jl, all the functions available there can be used in Surrogates.jl.

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