SciML / Surrogates.jl
Licence: other
Surrogate modeling and optimization for scientific machine learning (SciML)
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julia
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Surrogates.jl
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:
- Sample selection
- Construction of the surrogate model
- 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
- Lobachesky
- 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
- DYCORS
- EI
- SOP
- Multi-optimization: SMB and RTEA
Installing Surrogates package
using Pkg
Pkg.add("Surrogates")
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