Introduction to Multiphysics in Engineering: Multi-physics phenomena are central in many industrial applications and production lines, being directly connected to the efficiency and safety of the processes or the integrity of the produced parts. Embedded in process chains and engineering design tasks, multiphysics simulations successively become subject to tremendous automation and optimizations. This way, they are parts of Digital Twins, i.e., virtual, functional versions of systems that contain their properties and represent the life cycle of a system in the form of data and meta-data.
Using AI and ML in Engineering: However, this data must be combined and exploited correctly, where artificial intelligence (AI) and machine learning (ML) carry vast potential. For example, fast-running data-based surrogate models or automated evaluation of heterogeneous data and comparison/validation procedures, as well as clever calibration of uncertain simulation quantities, are possible through AI. For this, it is essential to be able to merge and reuse data of various kinds from various sources. Therefore, the digital twins can only be formed based on a solid data-interoperability backbone. The latter includes a machine-interpretable semantic meaning of the data and meta-data and it includes interfaces and data formats, as well as access control and the necessary processing software. Only when expertise and solutions from AI, semantics, data management, and engineering come together, the full potential of digitization and AI-enhanced digital twins can show advantage.