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.
Features
- Sample selection
- Construction of the surrogate model
- Surrogate optimization
- Sampling can be done through QuasiMonteCarlo.jl
- Second Order Polynomial
- Gradient Enhanced Kriging
Categories
Data VisualizationLicense
MIT LicenseFollow Surrogates.jl
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