Model order reduction and digital twins

Abstract

We are currently facing a substantial transformation of our industrial world and the way our economics are organized. This transformation, known as digitalization, is driven by the systemic integration of information technology in all kinds of devices, machines, and factories such that new smart networks are formed and new smart products have the ability to monitor, to forecast, and to control their behavior. One of the fundamental pillars of digitalization is simulation technology, since it enables the new intelligence layer in the form of digital twins which mirror the physical systems into the digital world - also named by Gartner Inc. as a top technology trend for 2017 and 2018. Creating such intelligence layers over several domains and life cycle phases requires, among other challenges, technologies for transforming and reducing complex simulation models. Exactly for this task a key technology is model order reduction (MOR). However, MOR is not only a key technology within emerging digital twins but also helps to reduce simulation times in the existing everyday business of simulation engineers. This is especially important when for a simulation model a large number of evaluations are needed. Within this chapter we present use cases where MOR is a key enabler for the realization of digital services and the reduction of simulation times. Furthermore we outline the potential of MOR in the context of realizing the digital twin vision.