The exact, inner working of the simulation code is not assumed to be known (or even understood), relying solely on the input-output behavior.
The process comprises three major steps which may be interleaved iteratively: The accuracy of the surrogate depends on the number and location of samples (expensive experiments or simulations) in the design space.
Popular surrogate modeling approaches are: polynomial response surfaces; kriging; more generalized Bayesian approaches;[1] gradient-enhanced kriging (GEK); radial basis function; support vector machines; space mapping;[2] artificial neural networks and Bayesian networks.
[2][7] Recently proposed comparison-based surrogate models (e.g., ranking support vector machines) for evolutionary algorithms, such as CMA-ES, allow preservation of some invariance properties of surrogate-assisted optimizers:[8] An important distinction can be made between two different applications of surrogate models: design optimization and design space approximation (also known as emulation).
[9] In design space approximation, one is not interested in finding the optimal parameter vector, but rather in the global behavior of the system.
Here the surrogate is tuned to mimic the underlying model as closely as needed over the complete design space.