An object-oriented architecture to couple simulators and their machine learning surrogates models in the context of digital shadows

Abstract

This article studies a method to couple two digital models in the context of digital twins. The first model is a simulation model which is supposed to be very accurate but computationally intensive. The second is a fast but approximate machine-learning model of the simulation. Both models serve, therefore, the same prediction task in an online environment but have different advantages and drawbacks. An object-oriented architecture is introduced to implement the proposed coupling strategy. Numerical experiment results on four datasets are also provided to evaluate the performances of the proposed strategy and compare it with a baseline. Three of these datasets originate from the University of California, Irvine machine learning repository. The last one originates from the Canadian forest product industry and contains the outputs of sawing simulation for real wood logs. These experiments demonstrate that the proposed method allows to consistently reduce the average error of the couple predictions.