Abstract: A method includes training a machine learning model on a training data set, that describes input parameters to and corresponding output parameters from manufacturing equipment, using at least one learning algorithm to obtain a physics model that describes evolution of a state space of the manufacturing equipment, configuring a machine-learning-based controller agent to generate commands for the physics model that modify settings of a simulation of the manufacturing equipment by the physics model such that, responsive to input data, the physics model generates corresponding predicted output parameters, and training the machine-learning-based controller agent on the settings and corresponding predicted output parameters using at least one other learning algorithm.

Abstract: Following activation of a first machine, a standardized structured data model is instantiated in a controller of the first machine that describes the first machine according to predefined categories populated with predefined labels that are indicative of measured parameters of the first machine, components of the first machine, and subsystems of the first machine.