1. A method for a design of experiments for modeling the effects of two or more input variables on one or more output variables, the method comprising the steps of: (a) generating a data set comprising data points from historical data for the input variables and the output variables, each data point comprising corresponding values for one or more of the input variables and one or more of the output variables; (b) identifying fault data points in the historical data, a fault data point being a data point in which an output variable value is determined to be caused by factors other than the input variables; (c) removing the identified fault data points from the data set, thereby generating a revised data set with data points; (d) supplying the data points from the revised data set into a nonlinear neural network model; and (e) deriving a simulator model characterizing a relationship between the input variables and the output variables using the nonlinear neural network model with the supplied data.