The paper presents Function+Data Flow (FDF), a visual domain-specific language for specifying and reusing machine learning pipelines that build digital twins in real time. It is implemented in DesCartes Builder, an integrated modeling environment supporting FDF-based twin synthesis and validation. An empirical user study indicates that this approach makes AI-based digital twin development more accessible and reliable.
The news matters for modeling agenda by offering tools that reduce complexity and improve reliability in creating AI-based digital twins.
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