DATA-EFFICIENT MULTI-ACQUISITION STRATEGY FOR SELECTING HIGH-COST COMPUTATIONAL OBJECTIVE FUNCTIONS

A method of optimizing parameters for an industrial process is described, along with media and systems, using a digital twin, physics based model and multiple types of acquisition functions. Output data from the model is analyzed by multiple types of Bayesian acquisition functions, such as an expected improvement acquisition function and a model variance acquisition function. The different acquisition functions tune better parameters, and then then model is re-run in parallel for each to output more data. The data from one acquisition function's run of the model may be co-mingled with data from the other acquisition function's run of the model such that the acquisition functions' exploration and exploitation of the parameter space are intertwined, thus achieving a more globally optimal solution than using just one type of Bayesian acquisition function.