1. A non-transitory computer readable memory medium that stores computer executable instructions that when executed by a processor perform model predictive control and optimization of a nonlinear process by implementing a parametric universal nonlinear dynamic approximator for predictive control and optimization of a nonlinear process, comprising: a dynamic parameterized model, operable to model the nonlinear process, wherein the dynamic parameterized model receives one or more parameters that are not inputs or outputs of the nonlinear process, and wherein the one or more parameters are outputs of an explicit mapping to a parameter space; and a nonlinear approximator, operable to explicitly model dependencies of the one or more parameters of the dynamic parameterized model upon operating conditions of the nonlinear process; wherein the parametric universal nonlinear dynamic approximator is operable to predict process outputs necessary for predictive control and optimization of the nonlinear process, wherein actual measurements of at least one of the process outputs do not exist, by: operating the nonlinear approximator to: receive one or more process operating conditions, including one or more process inputs; and generate values for the one or more parameters of the dynamic parameterized model based on the process operating conditions; and provide the values for the one or more parameters to the dynamic parameterized model; and operating the dynamic parameterized model to: receive the values of the one or more parameters from the nonlinear approximator; receive the one or more process inputs; generate one or more predicted process outputs based on the received values of the one or more parameters and the received one or more process inputs; and store the one or more predicted process outputs; wherein the parametric universal nonlinear dynamic approximator is operable to be coupled to the nonlinear process, wherein the parametric universal nonlinear dynamic approximator is further operable to be coupled to a control process, wherein the control process is operable to: a) initialize a parametric universal nonlinear dynamic approximator to a current status of the nonlinear process, comprising process inputs and outputs, by: initializing inputs to a nonlinear approximator comprised in the parametric universal nonlinear dynamic approximator, wherein the nonlinear approximator is trained to model dependencies of one or more parameters of a dynamic parameterized model of the nonlinear process comprised in the parametric universal nonlinear dynamic approximator upon operating conditions of the nonlinear process; executing the trained nonlinear approximator to determine initial values for the one or more parameters of the dynamic parameterized model based on the current status of the nonlinear process; and initializing the parameterized dynamic model with the determined initial values for the one or more parameters; b) formulate an optimization problem, including specifying an objective function for optimization of the nonlinear process; c) generate a profile of manipulated variables for the nonlinear process over a control horizon in accordance with the specified objective function for optimization of the nonlinear process; d) operate the parametric universal nonlinear dynamic approximator in accordance with the generated profile of manipulated variables, thereby generating predicted outputs for the nonlinear process; e) determine a deviation of the predicted outputs from a desired behavior of the nonlinear process; f) repeat b)- e) one or more times to determine an optimal profile of manipulated variables in accordance with the specified objective function for optimization of the nonlinear process; g) operate the nonlinear process in accordance with the optimal profile of manipulated variables, thereby generating process output; and repeat a)- g) one or more times to dynamically control the nonlinear process.