Active learning confidence measures for coupling strategies in digital twins integrating simulation and data-driven submodels

Many challenges have been raised in the scientific literature regarding the development of digital twins that can predict future states of production processes from data streams. This study is concerned with the coordination of several of their submodels to balance precision with computational requirements. A method to use stream-based active learning sampling strategies to couple two such models is proposed. Both models perform the same prediction task but have different advantages and disadvantages. The first is a simulation model that is supposed to have high fidelity level, but to be slow. The second is a machine learning model, which is fast but less accurate and requires many labeled examples to be trained on, which may require a lot of time and effort to gather. The objective is to leverage confidence measures in the predictions of the machine learning model. These measures are used to couple the two models and take advantage of their respective strengths. In particular, the aim is to reduce the digital twin's average prediction error while operating under limited computational capacity. Moreover, an application within the sawmill industry and numerical experiments are presented.