Abstract:

The development and prosperity of the Industrial Internet have brought a novel paradigm to the academic and industrial communities - Data-intensive Scientific Discovery. The modeling method of fusion physics mechanism and data driven is one of the research hotspots,which can provide efficient and flexible analysis tools for future digital twin system. This approach can benefit from both mechanism simulation (interpretability and generalization ability) and data-driven model (flexibility and learning ability), especially in the deep learning architecture. In this context, this paper focuses on the mechanism + data fusion method in the digital twin system for Industrial Internet. Firstly, the basic mathematical principles and modeling methods are established, while the differences between mechanism + data fusion modeling and traditional models are compared. Then, machine-learning model selection, physics mechanism constraints, and actual task requirements are introduced in detail, and the recent research progress and development are summarized. Finally, the actual application scenarios of this method are reported from three perspectives, including design optimization, manufacturing, and operation maintenance.

Key words: deep learning, mechanism simulation, data driven, physics-informed neural network, digital twin