Computer modeling is designed to predict the behavior of complex systems by solving the corresponding mathematical equations of the physical process. The simulation requires a huge number of parameters to be found, and they all require a large number of simulation runs, where each run takes a different combination of design parameters as input. Computer simulations tend to be expensive. Research that requires a large number of computer calculations will lead to exorbitant computational costs, which will make them practically impracticable. This is what surrogate modeling is for. Surrogate modeling builds a statistical model to accurately approximate the simulation result. Subsequently, this trained model can replace the original computer simulation when performing system analysis. © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).