Reluctant First Principles Models

Computer implemented methods and systems generate an improved predicted model of an industrial process or process engineering system. The model is a function of measurable features of the subject process and selected first principle features. First principle features are selected that capture linearities in a residual of a linear model constructed using a received dataset of the subject process. The model can further be a function of a scaled spline. The scaled spline is generated by computing a spine for a measurable feature of the subject process, fitting the computer spline to the residual of the constructed linear model, and scaling the fitting spline with a scaling factor. The model results in improved predictions of behavior of the subject process by relying primarily on the data of the measurable features of the subject process.