Physical system control optimization using principle models and machine learning models

Abstract

A computer implemented method for controlling determining an optimization solution for controlling a physical system. An optimization model is formed using an objective function and a set of constraints, a machine learning model that predicts a target value for a target variable for a physical system in response to receiving inputs for input variables for the physical system, and a first principle model that predicts the target value for the target variable for the physical system in response to receiving the inputs for the input variables for the physical system. Set points are determined for the input variables for an extremum for the target value for the target variable in the optimization model using regions in which the set points result in agreement between the target value predicted by the machine learning model and the target value predicted by the first principle model. The set points form the optimization solution.