Rate variables and auxiliary variables in System Dynamics models are normally constructed using functional equations and or table functions. To construct functions, however, it is imperative to know the underlying relation between the independent variables and the dependent variable. This we know is not always an easy task. Indeed, in many differentially non-linear or chaotic situations this may be totally impossible. One may have to resort to less accurate representations if constrained to write relations as equations or tables. Neural Networks has been deployed in many fields to capture the underlying structural relations between variables in such situations through training schemes. When trained, Neural Networks may achieve generalization capabilities though literarily as black boxes. As Neural Networks models when trained can work online like a function, they can be easily implanted within System Dynamics models to compute rates or auxiliary variables. The idea in this article is, in situations were it is not possible or it is considerably difficult to construct explicit functions or tables, to deploy Neural Networks to surrogate fuctions. Neural Network models, here called elements, can be trained on actual data to capture the underlying functional relationships between input output variables and implanted as rates or auxiliary variables to carry out computation on line.