  While the digital twin has become an intrinsic part of the product creation
process, its true power lies in the connectivity of the digital representation
with its physical counterpart. Data acquired on the physical asset can
validate, update and enrich the digital twin. The knowledge contained in the
digital representation brings value to the physical asset itself. When a
dedicated encapsulation is extracted from the digital twin to model a specific
set of behaviors in a specific context, delivering a stand-alone executable
representation, such instantiated and self-contained model is referred to as an
Executable Digital Twin. In this contribution, key building blocks such as
model order reduction, real-time models, state estimation and co-simulation are
reviewed, and a number of characteristic use cases are presented. These include
virtual sensing, hybrid testing and hardware-in-the loop, model-based control
and model-based diagnostics.
