So the idea of surrogate models (for parameter estimation) has been around for some time, where f(x, θ) is some (computationally) simplified model of a complex

So the idea of surrogate models (for parameter estimation) has been around for some time, where f(x, θ) is some (computationally) simplified model of a complex model/simulation (x = factors, θ = parameters). f can be any arbitrary choice that works. Not sure if the choice of f being a NN is necessarily related to AI, where some cognitive function is being replicated. It is a good function approximator though.